1353  
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Break
Tue Jul 10th 08:45 -- 09:15 AM @ Hall B
Coffee Break
Tutorial
Tue Jul 10th 09:15 -- 11:30 AM @ Victoria
Imitation Learning
Yisong Yue · Hoang M Le
Tutorial
Tue Jul 10th 09:15 -- 11:30 AM @ K1 + K2
Learning with Temporal Point Processes
Manuel Gomez Rodriguez · Isabel Valera
Tutorial
Tue Jul 10th 09:15 -- 11:30 AM @ A9
Machine Learning in Automated Mechanism Design for Pricing and Auctions
Nina Balcan · Tuomas Sandholm · Ellen Vitercik
Break
Tue Jul 10th 11:30 AM -- 01:00 PM @
Lunch - on your own
Tutorial
Tue Jul 10th 01:00 -- 03:15 PM @ Victoria
Toward Theoretical Understanding of Deep Learning
Sanjeev Arora
Tutorial
Tue Jul 10th 01:00 -- 03:15 PM @ K1 + K2
Defining and Designing Fair Algorithms
Sam Corbett-Davies · Sharad Goel
Tutorial
Tue Jul 10th 01:00 -- 03:15 PM @ A9
Understanding your Neighbors: Practical Perspectives From Modern Analysis
Sanjoy Dasgupta · Samory Kpotufe
Break
Tue Jul 10th 03:15 -- 03:45 PM @ Hall B
Coffee Break
Tutorial
Tue Jul 10th 03:45 -- 06:00 PM @ Victoria
Variational Bayes and Beyond: Bayesian Inference for Big Data
Tamara Broderick
Tutorial
Tue Jul 10th 03:45 -- 06:00 PM @ K1 + K2
Machine Learning for Personalised Health
Danielle Belgrave · Konstantina Palla · LAMIAE Azizi
Tutorial
Tue Jul 10th 03:45 -- 06:00 PM @ A9
Optimization Perspectives on Learning to Control
Benjamin Recht
Break
Tue Jul 10th 06:00 -- 07:15 PM @ Hall B
Opening Reception
Invited Talk
Wed Jul 11th 09:00 -- 10:00 AM @ A1
AI and Security: Lessons, Challenges and Future Directions
Dawn Song
Break
Wed Jul 11th 10:30 -- 11:00 AM @ Hall B
Coffee Break
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A3
Transfer Learning via Learning to Transfer
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A1
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A6
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A4
Crowdsourcing with Arbitrary Adversaries
Oral
Wed Jul 11th 11:00 -- 11:10 AM @ K1+K2
Nonoverlap-Promoting Variable Selection
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ Victoria
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Oral
Wed Jul 11th 11:00 -- 11:10 AM @ K11
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A9
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A7
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Oral
Wed Jul 11th 11:10 -- 11:20 AM @ K1+K2
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Oral
Wed Jul 11th 11:10 -- 11:20 AM @ K11
Hierarchical Clustering with Structural Constraints
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A7
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A1
Learning with Abandonment
Oral
Wed Jul 11th 11:20 -- 11:40 AM @ A3
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A4
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Oral
Wed Jul 11th 11:20 -- 11:40 AM @ A5
SparseMAP: Differentiable Sparse Structured Inference
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ K11
K-means clustering using random matrix sparsification
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A9
Distributed Nonparametric Regression under Communication Constraints
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ K1+K2
Black Box FDR
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A6
Do Outliers Ruin Collaboration?
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ Victoria
Learning to search with MCTSnets
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ K11
Clustering Semi-Random Mixtures of Gaussians
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A4
Conditional Noise-Contrastive Estimation of Unnormalised Models
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A6
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A7
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A1
Lipschitz Continuity in Model-based Reinforcement Learning
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A9
Coded Sparse Matrix Multiplication
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ Victoria
Differentiable plasticity: training plastic neural networks with backpropagation
Oral
Wed Jul 11th 11:30 -- 11:50 AM @ K1+K2
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A4
Deep One-Class Classification
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ Victoria
TACO: Learning Task Decomposition via Temporal Alignment for Control
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A1
Implicit Quantile Networks for Distributional Reinforcement Learning
Oral
Wed Jul 11th 11:40 AM -- 12:00 PM @ K11
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A9
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A5
Efficient and Consistent Adversarial Bipartite Matching
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A3
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A7
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A6
Variational Network Inference: Strong and Stable with Concrete Support
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A3
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Elliot Meyerson · Risto Miikkulainen
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A9
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A6
Network Global Testing by Counting Graphlets
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A7
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ Victoria
Graph Networks as Learnable Physics Engines for Inference and Control
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A5
Learning to Speed Up Structured Output Prediction
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ K1+K2
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A1
More Robust Doubly Robust Off-policy Evaluation
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A4
Deep Density Destructors
Break
Wed Jul 11th 12:00 -- 01:30 PM @
Lunch - on your own
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A5
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ Victoria
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A1
Coordinated Exploration in Concurrent Reinforcement Learning
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A6
Data-Dependent Stability of Stochastic Gradient Descent
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ K11
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A9
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A7
Improving Optimization in Models With Continuous Symmetry Breaking
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ K1+K2
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A4
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A3
Stagewise Safe Bayesian Optimization with Gaussian Processes
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A4
Bayesian Quadrature for Multiple Related Integrals
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A9
signSGD: Compressed Optimisation for Non-Convex Problems
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ K11
Subspace Embedding and Linear Regression with Orlicz Norm
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A3
BOCK : Bayesian Optimization with Cylindrical Kernels
Oral
Wed Jul 11th 01:50 -- 02:00 PM @ A1
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A5
Nearly Optimal Robust Subspace Tracking
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A6
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ K1+K2
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ Victoria
Semi-Supervised Learning via Compact Latent Space Clustering
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A7
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Oral
Wed Jul 11th 02:00 -- 02:10 PM @ A1
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A7
Learning Steady-States of Iterative Algorithms over Graphs
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A9
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A4
Differentiable Compositional Kernel Learning for Gaussian Processes
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ K1+K2
An Estimation and Analysis Framework for the Rasch Model
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A5
Safe Element Screening for Submodular Function Minimization
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A6
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ K11
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ Victoria
Conditional Neural Processes
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A1
Gated Path Planning Networks
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A3
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A3
Bayesian Optimization of Combinatorial Structures
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A4
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A1
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ K1+K2
End-to-end Active Object Tracking via Reinforcement Learning
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A9
$D^2$: Decentralized Training over Decentralized Data
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ Victoria
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ K11
Streaming Principal Component Analysis in Noisy Setting
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A6
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A7
Anonymous Walk Embeddings
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A5
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A6
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A7
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ K11
Linear Spectral Estimators and an Application to Phase Retrieval
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A4
Variational Inference and Model Selection with Generalized Evidence Bounds
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ Victoria
Non-linear motor control by local learning in spiking neural networks
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A5
The Limits of Maxing, Ranking, and Preference Learning
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ K1+K2
Deep Predictive Coding Network for Object Recognition
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A9
An Alternative View: When Does SGD Escape Local Minima?
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A1
Structured Control Nets for Deep Reinforcement Learning
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A3
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A1
Latent Space Policies for Hierarchical Reinforcement Learning
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ K11
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A9
Escaping Saddles with Stochastic Gradients
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A4
Fixing a Broken ELBO
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A6
The Generalization Error of Dictionary Learning with Moreau Envelopes
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A7
Disentangling by Factorising
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ Victoria
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A3
Selecting Representative Examples for Program Synthesis
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A5
Learning a Mixture of Two Multinomial Logits
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ K1+K2
Gradually Updated Neural Networks for Large-Scale Image Recognition
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ Victoria
Hierarchical Long-term Video Prediction without Supervision
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A7
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A6
On Learning Sparsely Used Dictionaries from Incomplete Samples
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ K1+K2
Neural Inverse Rendering for General Reflectance Photometric Stereo
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A5
The Weighted Kendall and High-order Kernels for Permutations
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ Victoria
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A9
Stochastic Variance-Reduced Cubic Regularized Newton Method
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A3
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A1
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A7
Learning Independent Causal Mechanisms
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ K11
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ K1+K2
One-Shot Segmentation in Clutter
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A6
The Well-Tempered Lasso
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A5
Parameterized Algorithms for the Matrix Completion Problem
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A4
Tighter Variational Bounds are Not Necessarily Better
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ K11
Testing Sparsity over Known and Unknown Bases
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A9
Non-convex Conditional Gradient Sliding
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A1
An Inference-Based Policy Gradient Method for Learning Options
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ K1+K2
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A6
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A4
Continuous-Time Flows for Efficient Inference and Density Estimation
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A7
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ Victoria
Model-Level Dual Learning
Break
Wed Jul 11th 03:30 -- 04:00 PM @ Hall B
Coffee Break
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A9
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A5
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ Victoria
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ K1+K2
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A6
Differentially Private Matrix Completion Revisited
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ K11
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A1
Programmatically Interpretable Reinforcement Learning
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A4
Semi-Implicit Variational Inference
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A3
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A7
Which Training Methods for GANs do actually Converge?
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A4
Efficient Gradient-Free Variational Inference using Policy Search
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ K1+K2
The Dynamics of Learning: A Random Matrix Approach
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A1
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A7
Chi-square Generative Adversarial Network
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ Victoria
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ A9
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ K11
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A3
Feedback-Based Tree Search for Reinforcement Learning
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ A6
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A5
Representation Learning on Graphs with Jumping Knowledge Networks
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ A6
Local Private Hypothesis Testing: Chi-Square Tests
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ A9
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ Victoria
Kronecker Recurrent Units
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A5
Learning Diffusion using Hyperparameters
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A3
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A1
Automatic Goal Generation for Reinforcement Learning Agents
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A7
Learning Implicit Generative Models with the Method of Learned Moments
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ K11
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A6
Locally Private Hypothesis Testing
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A4
A Spectral Approach to Gradient Estimation for Implicit Distributions
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A9
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ Victoria
Fast Parametric Learning with Activation Memorization
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ K1+K2
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A1
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A9
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A6
INSPECTRE: Privately Estimating the Unseen
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ K11
Data Summarization at Scale: A Two-Stage Submodular Approach
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A7
A Classification-Based Study of Covariate Shift in GAN Distributions
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A3
Learning the Reward Function for a Misspecified Model
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ Victoria
Dynamic Evaluation of Neural Sequence Models
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A4
Quasi-Monte Carlo Variational Inference
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ K1+K2
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A5
Canonical Tensor Decomposition for Knowledge Base Completion
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A6
Delayed Impact of Fair Machine Learning
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A5
Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang · Heinz Koeppl
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A4
Yes, but Did It Work?: Evaluating Variational Inference
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ K11
Learning to Optimize Combinatorial Functions
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A7
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A1
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A3
Machine Theory of Mind
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A9
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ Victoria
Decoupled Parallel Backpropagation with Convergence Guarantee
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ K1+K2
Essentially No Barriers in Neural Network Energy Landscape
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ Victoria
Efficient Neural Architecture Search via Parameters Sharing
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A1
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A3
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A6
Fairness Without Demographics in Repeated Loss Minimization
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ A9
Fast Variance Reduction Method with Stochastic Batch Size
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ K1+K2
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A4
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A7
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ K11
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A5
NetGAN: Generating Graphs via Random Walks
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ K11
Binary Partitions with Approximate Minimum Impurity
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ K1+K2
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ A9
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A6
Nonconvex Optimization for Regression with Fairness Constraints
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ Victoria
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ K11
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A7
Synthesizing Robust Adversarial Examples
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A1
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A3
Been There, Done That: Meta-Learning with Episodic Recall
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A9
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ K1+K2
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A4
Black-Box Variational Inference for Stochastic Differential Equations
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A5
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A6
Fair and Diverse DPP-Based Data Summarization
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A1
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A9
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A5
Neural Relational Inference for Interacting Systems
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ K1+K2
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ Victoria
Spline Filters For End-to-End Deep Learning
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A4
Inference Suboptimality in Variational Autoencoders
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A7
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A3
Continual Reinforcement Learning with Complex Synapses
Break
Wed Jul 11th 06:15 -- 07:15 PM @ Hall B
Light Evening Snack
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #1
Spline Filters For End-to-End Deep Learning
Randall Balestriero · Romain Cosentino · Herve Glotin · Richard Baraniuk
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #2
Non-linear motor control by local learning in spiking neural networks
Aditya Gilra · Wulfram Gerstner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #3
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney · Georg Ostrovski · David Silver · Remi Munos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #4
An Inference-Based Policy Gradient Method for Learning Options
Matthew Smith · Herke van Hoof · Joelle Pineau
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim · Amir Globerson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #6
Differentially Private Matrix Completion Revisited
Prateek Jain · Om Dipakbhai Thakkar · Abhradeep Thakurta
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #7
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi · Kenneth Stanley · Jeff Clune
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #8
Model-Level Dual Learning
Yingce Xia · Xu Tan · Fei Tian · Tao Qin · Nenghai Yu · Tie-Yan Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #9
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian · Teng Zhang · James Zou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #10
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Benjamin Paaßen · Claudio Gallicchio · Alessio Micheli · CITEC Barbara Hammer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #11
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao · Zhishuai Zhang · Wei Shen · Bo Wang · Alan Yuille
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #12
One-Shot Segmentation in Clutter
Claudio Michaelis · Matthias Bethge · Alexander Ecker
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #13
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen · Deva Ramanan · Charless Fowlkes
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #14
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furong Huang · Jordan Ash · John Langford · Robert Schapire
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #15
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes · Yu Xuan Liu · Abhishek Gupta · Benjamin Eysenbach · Pieter Abbeel · Sergey Levine
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #16
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette · Emma Brunskill
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #17
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Poorya Mianjy · Raman Arora
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #18
Subspace Embedding and Linear Regression with Orlicz Norm
Alexandr Andoni · Chengyu Lin · Ying Sheng · Peilin Zhong · Ruiqi Zhong
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #19
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Sreejith Kallummil · Sheetal Kalyani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #20
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Eric Wong · Zico Kolter
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #21
Learning the Reward Function for a Misspecified Model
Erik Talvitie
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #22
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
kyowoon Lee · Sol-A Kim · Jaesik Choi · Seong-Whan Lee
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #23
Do Outliers Ruin Collaboration?
Mingda Qiao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #24
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Wenlong Mou · Yuchen Zhou · Jun Gao · Liwei Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #25
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
IEMS Xingyu Wang · Diego Klabjan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #26
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #27
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Guillaume Pouliot
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #28
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang · Jennifer Jang · Samory Kpotufe
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #29
Learning Diffusion using Hyperparameters
Dimitrios Kalimeris · Yaron Singer · Karthik Subbian · Udi Weinsberg
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #30
Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti · Ravi Kumar · Andrew Tomkins
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #31
Crowdsourcing with Arbitrary Adversaries
Matthäus Kleindessner · Pranjal Awasthi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #32
Deep Density Destructors
David Inouye · Pradeep Ravikumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #33
Programmatically Interpretable Reinforcement Learning
Abhinav Verma · Vijayaraghavan Murali · Rishabh Singh · Pushmeet Kohli · Swarat Chaudhuri
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #34
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski · Mark Rowland · Vikas Sindhwani · Richard E Turner · Adrian Weller
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #35
The Weighted Kendall and High-order Kernels for Permutations
Yunlong Jiao · Jean-Philippe Vert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #36
The Limits of Maxing, Ranking, and Preference Learning
Moein Falahatgar · Ayush Jain · Alon Orlitsky · Venkatadheeraj Pichapati · Vaishakh Ravindrakumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #37
Black Box FDR
Wesley Tansey · Yixin Wang · David Blei · Raul Rabadan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #38
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Mao Ye · Yan Sun
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #39
Clustering Semi-Random Mixtures of Gaussians
Aravindan Vijayaraghavan · Pranjal Awasthi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #40
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Charlie Dickens · Graham Cormode · David Woodruff
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #41
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Martin Riedmiller · Roland Hafner · Thomas Lampe · Michael Neunert · Jonas Degrave · Tom Van de Wiele · Vlad Mnih · Nicolas Heess · Jost Springenberg
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #42
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji · Jian Zhang · Ruslan Salakhutdinov
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #43
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #44
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista · Matthias Poloczek
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #45
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You · Rex (Zhitao) Ying · Xiang Ren · Will Hamilton · Jure Leskovec
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #46
Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang · Heinz Koeppl
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #47
K-means clustering using random matrix sparsification
Kaushik Sinha
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #48
Hierarchical Clustering with Structural Constraints
Evangelos Chatziafratis · Rad Niazadeh · Moses Charikar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #49
Kronecker Recurrent Units
Cijo Jose · Mouhamadou Moustapha Cisse · Francois Fleuret
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #50
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas · Daniel C. Castro · Loic Le Folgoc · Ian Walker · Ryutaro Tanno · Daniel Rueckert · Ben Glocker · Antonio Criminisi · Aditya Nori
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #51
Dynamic Evaluation of Neural Sequence Models
Ben Krause · Emmanuel Kahembwe · Iain Murray · Steve Renals
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #52
TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis · Markus Wulfmeier · Sasha Salter · Shimon Whiteson · Herbert Ingmar Posner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #53
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi · Shengyang Sun · Jun Zhu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #54
Quasi-Monte Carlo Variational Inference
Alexander Buchholz · Florian Wenzel · Stephan Mandt
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #55
Learning to Optimize Combinatorial Functions
Nir Rosenfeld · Eric Balkanski · Amir Globerson · Yaron Singer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #56
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agarwal · Morteza Zadimoghaddam · Vahab Mirrokni
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #57
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu · Chengtao Li · Yonglong Tian · Tomohiro Sonobe · Ken-ichi Kawarabayashi · Stefanie Jegelka
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #58
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski · Oleksandr Shchur · Daniel Zügner · Stephan Günnemann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #59
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya · Gautam Kamath · Ziteng Sun · Huanyu Zhang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #60
Locally Private Hypothesis Testing
Or Sheffet
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #61
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja · Kristian Hartikainen · Pieter Abbeel · Sergey Levine
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #62
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar · Yinlam Chow · Mohammad Ghavamzadeh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #63
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #64
End-to-end Active Object Tracking via Reinforcement Learning
Wenhan Luo · Peng Sun · Fangwei Zhong · Wei Liu · Tong Zhang · Yizhou Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #65
Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony · Sima Behpour · Xinhua Zhang · Brian Ziebart
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #66
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae · Andre Filipe Torres Martins · Mathieu Blondel · Claire Cardie
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #67
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi · Paolo Frasconi · Saverio Salzo · Riccardo Grazzi · Massimiliano Pontil
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #68
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit · Ron Meir
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #69
Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian · DePaul Iyad Kanj · Sebastian Ordyniak · Stefan Szeider
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #70
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy · Iowa Namrata Vaswani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #71
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
Zeyuan Allen-Zhu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #72
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein · Yu-Xiang Wang · Kamyar Azizzadenesheli · Anima Anandkumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #73
Synthesizing Robust Adversarial Examples
Anish Athalye · Logan Engstrom · Andrew Ilyas · Kevin Kwok
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #74
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman · Timon Gehr · Martin Vechev
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #75
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen · Jun Zhu · Le Song
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #76
Neural Relational Inference for Interacting Systems
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #77
Which Training Methods for GANs do actually Converge?
Lars Mescheder · Andreas Geiger · Sebastian Nowozin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #78
Learning Independent Causal Mechanisms
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #79
Nonconvex Optimization for Regression with Fairness Constraints
Junpei Komiyama · Akiko Takeda · Junya Honda · Hajime Shimao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #80
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori Hashimoto · Megha Srivastava · Hongseok Namkoong · Percy Liang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #81
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Bo Zhao · Xinwei Sun · Yanwei Fu · Yuan Yao · Yizhou Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #82
Nonoverlap-Promoting Variable Selection
Pengtao Xie · Hongbao Zhang · Yichen Zhu · Eric Xing
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #83
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen · Aryan Mokhtari · Tengfei Zhou · Peilin Zhao · Hui Qian
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #84
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez · Nicolas Heess · Jost Springenberg · Josh Merel · Martin Riedmiller · Raia Hadsell · Peter Battaglia
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #85
An Alternative View: When Does SGD Escape Local Minima?
Bobby Kleinberg · Yuanzhi Li · Yang Yuan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #86
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian · Wei Zhang · Ce Zhang · Ji Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #87
An Estimation and Analysis Framework for the Rasch Model
Andrew Lan · Mung Chiang · Christoph Studer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #88
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth V Neel · Aaron Roth
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #89
Local Private Hypothesis Testing: Chi-Square Tests
Marco Gaboardi · Ryan Rogers
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #90
Disentangling by Factorising
DeepMind Hyunjik Kim · Andriy Mnih
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #91
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #92
Learning to search with MCTSnets
Arthur Guez · Theophane Weber · Ioannis Antonoglou · Karen Simonyan · Oriol Vinyals · Daan Wierstra · Remi Munos · David Silver
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #93
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo · Bin Gu · Qian Yang · Heng Huang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #94
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh Nguyen · Akshay Soni · Chinmay Hegde
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #95
Variational Network Inference: Strong and Stable with Concrete Support
Amir Dezfouli · Edwin Bonilla · Richard Nock
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #96
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen · Moran Feldman · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #97
Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic · Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #98
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Chao Tao · Saúl A. Blanco · Yuan Zhou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #99
Learning with Abandonment
Sven Schmit · Ramesh Johari
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #100
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea · Gary Becigneul · Thomas Hofmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #101
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro · Danilo J. Rezende · Yori Zwols · Alexander Pritzel · S. M. Ali Eslami · Fabio Viola
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #102
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster · Gregory Farquhar · Maruan Al-Shedivat · Tim Rocktäschel · Eric Xing · Shimon Whiteson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #103
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich · Devin Willmott · Qiang Ye
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #104
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Stephen Tu · Benjamin Recht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #105
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Yuanxiang Gao · Department of Electrical and Computer Li Chen · Baochun Li
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #106
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #107
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou · Benjamin Van Roy
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #108
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno · Tetsuya Hada · Hidetoshi Shimodaira
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #109
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai · Zornitsa Kozareva · Bo Dai · Alex Smola · Le Song
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #110
Delayed Impact of Fair Machine Learning
Lydia T. Liu · Sarah Dean · Esther Rolf · Max Simchowitz · University of California Moritz Hardt
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #111
Fair and Diverse DPP-Based Data Summarization
Elisa Celis · Vijay Keswani · Damian Straszak · Amit Jayant Deshpande · Tarun Kathuria · Nisheeth Vishnoi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #112
Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri · Shakir Mohamed · Mihaela Rosca · Oriol Vinyals
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #113
Chi-square Generative Adversarial Network
Chenyang Tao · Liqun Chen · Ricardo Henao · Jianfeng Feng · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #114
Streaming Principal Component Analysis in Noisy Setting
Teodor Vanislavov Marinov · Poorya Mianjy · Raman Arora
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #115
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Jan-Hendrik Lange · Andreas Karrenbauer · Bjoern Andres
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #116
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen · PHUONG HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #117
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Pavel Dvurechenskii · Alexander Gasnikov · Alexey Kroshnin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #118
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles · Dimitris Papailiopoulos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #119
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin · Volkan Cevher
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #120
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer · Mitchell Stern
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #121
Fast Parametric Learning with Activation Memorization
Jack Rae · Chris Dyer · Peter Dayan · Timothy Lillicrap
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #122
Essentially No Barriers in Neural Network Energy Landscape
Felix Draxler · Kambis Veschgini · Manfred Salmhofer · Fred Hamprecht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #123
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent · James von Brecht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #124
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu · Jianfei Cai · Yi Wang · Yew Soon ONG
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #125
Bayesian Quadrature for Multiple Related Integrals
Xiaoyue Xi · Francois-Xavier Briol · Mark Girolami
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #126
Deep Predictive Coding Network for Object Recognition
Haiguang Wen · Kuan Han · Junxing Shi · Yizhen Zhang · Eugenio Culurciello · Zhongming Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #127
Neural Inverse Rendering for General Reflectance Photometric Stereo
Tatsunori Taniai · Takanori Maehara
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #128
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann · Percy Liang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #129
Selecting Representative Examples for Program Synthesis
Yewen Pu · Zachery Miranda · Armando Solar-Lezama · Leslie Kaelbling
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #130
Conditional Neural Processes
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #131
Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers · Ruben Villegas · Dumitru Erhan · Honglak Lee
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #132
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato · Brendan O'Donoghue · Pushmeet Kohli · Aäron van den Oord
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #133
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar · Ludwig Schmidt · Aleksander Madry
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #134
Gated Path Planning Networks
Lisa Lee · Emilio Parisotto · Devendra Singh Chaplot · Eric Xing · Ruslan Salakhutdinov
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #135
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa · David Held · Xinyang Geng · Pieter Abbeel
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #136
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme Franca · Daniel Robinson · Rene Vidal
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #137
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu · Stephen Wright · Laurent Lessard
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #138
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu · Federico Errica · Alessio Micheli
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #139
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximillian Nickel · Douwe Kiela
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #140
Fast Variance Reduction Method with Stochastic Batch Size
University of California Xuanqing Liu · Cho-Jui Hsieh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #141
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Adrien Taylor · Bryan Van Scoy · Laurent Lessard
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #142
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu · Hariank Muthakana · Sivaraman Balakrishnan · Aarti Singh · Artur Dubrawski
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #143
The Well-Tempered Lasso
Yuanzhi Li · Yoram Singer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #144
Transfer Learning via Learning to Transfer
Ying WEI · Yu Zhang · Junzhou Huang · Qiang Yang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #145
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Elliot Meyerson · Risto Miikkulainen
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #146
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura · Issei Sato · Masashi Sugiyama
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #147
Deep One-Class Classification
Lukas Ruff · Nico Görnitz · Lucas Deecke · Shoaib Ahmed Siddiqui · Robert Vandermeulen · Alexander Binder · Emmanuel Müller · Marius Kloft
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #148
Binary Partitions with Approximate Minimum Impurity
Eduardo Laber · Marco Molinaro · Felipe de A. Mello Pereira
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #149
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard · Jakub Tarnawski · Slobodan Mitrovic · Amir Zandieh · Aidasadat Mousavifar · Ola Svensson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #150
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao · Aki Vehtari · Daniel Simpson · Andrew Gelman
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #151
Black-Box Variational Inference for Stochastic Differential Equations
Tom Ryder · Andrew Golightly · Stephen McGough · Dennis Prangle
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #152
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing WANG · Quanming Yao · James Kwok · Lionel NI
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #153
Learning to Speed Up Structured Output Prediction
Xingyuan Pan · Vivek Srikumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #154
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Maryam Aliakbarpour · Ilias Diakonikolas · MIT Ronitt Rubinfeld
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #155
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Ibrahim Alabdulmohsin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #156
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh · Efstratios Gavves · Max Welling
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #157
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner · Aaron Klein · Frank Hutter
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #158
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu · John Lafferty
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #159
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Ganggang Xu · Zuofeng Shang · Guang Cheng
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #160
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Marine LE MORVAN · Jean-Philippe Vert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #161
Safe Element Screening for Submodular Function Minimization
Weizhong Zhang · Bin Hong · Lin Ma · Wei Liu · Tong Zhang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #162
Feedback-Based Tree Search for Reinforcement Learning
Daniel Jiang · Emmanuel Ekwedike · Han Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #163
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Andre Barreto · Diana Borsa · John Quan · Tom Schaul · David Silver · Matteo Hessel · Daniel J. Mankowitz · Augustin Zidek · Remi Munos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #164
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij · Christoph Lampert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #165
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #166
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #167
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen · Pan Xu · Lingxiao Wang · Jian Ma · Quanquan Gu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #168
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi · Levent Sagun · Mario Geiger · Stefano Spigler · Gerard Arous · Chiara Cammarota · Yann LeCun · Matthieu Wyart · Giulio Biroli
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #169
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Qianxiao Li · IHPC Shuji Hao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #170
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos · Francois Fleuret
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #171
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #172
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow · Ofir Nachum · Mohammad Ghavamzadeh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #173
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi · Dipendra Misra · Michael L. Littman
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #174
Linear Spectral Estimators and an Application to Phase Retrieval
Ramina Ghods · Andrew Lan · Tom Goldstein · Christoph Studer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #175
Testing Sparsity over Known and Unknown Bases
Siddharth Barman · Arnab Bhattacharyya · Suprovat Ghoshal
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #176
Inference Suboptimality in Variational Autoencoders
Chris Cremer · Xuechen Li · David Duvenaud
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #177
Semi-Implicit Variational Inference
Mingzhang Yin · Mingyuan Zhou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #178
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Hang Wu · May Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #179
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Ahmed M. Alaa Ibrahim · M van der Schaar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #180
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu · Zilu Zhang · Tal Friedman · Yitao Liang · Guy Van den Broeck
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #181
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Jiong Zhang · Qi Lei · Inderjit Dhillon
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #182
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Yancheng Yuan · Defeng Sun · Kim-Chuan Toh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #183
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Shuai Zheng · James Kwok
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #184
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Masanori SUGANUMA · Mete Ozay · Takayuki Okatani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #185
Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #186
Non-convex Conditional Gradient Sliding
chao qu · Yan Li · Huan Xu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #187
Stochastic Variance-Reduced Cubic Regularized Newton Method
Dongruo Zhou · Pan Xu · Quanquan Gu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #188
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora · Nadav Cohen · Elad Hazan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #189
The Dynamics of Learning: A Random Matrix Approach
Zhenyu Liao · Romain Couillet
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #190
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen · Martin Min · Yizhou Sun
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #191
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel · Zoubin Ghahramani · Adrian Weller
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #192
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen · Chunyuan Li · Liquan Chen · Wenlin Wang · Yunchen Pu · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #193
Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth · Adam Kosiorek · Tuan Anh Le · Chris Maddison · Maximilian Igl · Frank Wood · Yee Whye Teh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #194
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang · Zhifeng Gao · Mingsheng Long · Jianmin Wang · Philip Yu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #195
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon · James Jordon · Mihaela van der Schaar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #196
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #197
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Minyoung Kim
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #198
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Marc Abeille · Alessandro Lazaric
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #199
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson · Marc P Deisenroth · Ruth Misener
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #200
Anonymous Walk Embeddings
Sergey Ivanov · Evgeny Burnaev
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #201
Improving Optimization in Models With Continuous Symmetry Breaking
Robert Bamler · Stephan Mandt
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #202
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan · Michael Gutmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #203
Canonical Tensor Decomposition for Knowledge Base Completion
Timothee Lacroix · Nicolas Usunier · Guillaume R Obozinski
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #204
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Siyuan Ma · Raef Bassily · Mikhail Belkin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #205
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou · Fanhua Shang · James Cheng
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #206
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand · Jonas Kohler · Aurelien Lucchi · Thomas Hofmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #207
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang · Xiangru Lian · Ming Yan · Ce Zhang · Ji Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #208
Machine Theory of Mind
Neil Rabinowitz · Frank Perbet · Francis Song · Chiyuan Zhang · S. M. Ali Eslami · Matthew Botvinick
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #209
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter · Jane Wang · Zeb Kurth-Nelson · Siddhant Jayakumar · Charles Blundell · Razvan Pascanu · Matthew Botvinick
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #210
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Bin Gu · Zhouyuan Huo · Cheng Deng · Heng Huang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #211
Coded Sparse Matrix Multiplication
Sinong Wang · Jiashang Liu · Ness Shroff
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #212
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco Ruiz · Michalis Titsias · Adji Bousso Dieng · David Blei
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #213
Efficient Gradient-Free Variational Inference using Policy Search
Oleg Arenz · Gerhard Neumann · Mingjun Zhong
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #214
Fixing a Broken ELBO
Alexander Alemi · Ben Poole · Ian Fischer · Joshua V Dillon · Rif Saurous · Kevin Murphy
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #215
Variational Inference and Model Selection with Generalized Evidence Bounds
Liqun Chen · Chenyang Tao · RUIYI ZHANG · Ricardo Henao · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #216
The Generalization Error of Dictionary Learning with Moreau Envelopes
ALEXANDROS GEORGOGIANNIS
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #217
Network Global Testing by Counting Graphlets
Jiashun Jin · Zheng Ke · Shengming Luo
Invited Talk
Thu Jul 12th 09:00 -- 10:00 AM @ A1
Intelligence per Kilowatthour
Max Welling
Break
Thu Jul 12th 10:30 -- 11:00 AM @ Hall B
Coffee Break
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A9
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A4
Learning unknown ODE models with Gaussian processes
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A5
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A6
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ K11
Probabilistic Boolean Tensor Decomposition
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A7
Geometry Score: A Method For Comparing Generative Adversarial Networks
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ A1
Convergent Tree Backup and Retrace with Function Approximation
Oral
Thu Jul 12th 11:00 -- 11:10 AM @ A3
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ Victoria
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Oral
Thu Jul 12th 11:00 -- 11:20 AM @ K1
Learning Memory Access Patterns
Oral
Thu Jul 12th 11:10 -- 11:20 AM @ A3
Learning to Act in Decentralized Partially Observable MDPs
Oral
Thu Jul 12th 11:20 -- 11:30 AM @ K11
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Oral
Thu Jul 12th 11:20 -- 11:30 AM @ A7
Optimizing the Latent Space of Generative Networks
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ A4
Constraining the Dynamics of Deep Probabilistic Models
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ A1
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ A5
Differentiable Dynamic Programming for Structured Prediction and Attention
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ K1
Geodesic Convolutional Shape Optimization
Oral
Thu Jul 12th 11:20 -- 11:30 AM @ A3
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ A9
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Oral
Thu Jul 12th 11:20 -- 11:30 AM @ A6
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Oral
Thu Jul 12th 11:20 -- 11:40 AM @ Victoria
Compressing Neural Networks using the Variational Information Bottelneck
Oral
Thu Jul 12th 11:30 -- 11:40 AM @ K11
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Oral
Thu Jul 12th 11:30 -- 11:40 AM @ A6
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Oral
Thu Jul 12th 11:30 -- 11:40 AM @ A3
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Oral
Thu Jul 12th 11:30 -- 11:40 AM @ A7
Adversarial Learning with Local Coordinate Coding
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A4
Probabilistic Recurrent State-Space Models
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ Victoria
Kernelized Synaptic Weight Matrices
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A6
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ K11
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A7
Learning Representations and Generative Models for 3D Point Clouds
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A9
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A5
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ A1
Scalable Bilinear Pi Learning Using State and Action Features
Oral
Thu Jul 12th 11:40 -- 11:50 AM @ K1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Oral
Thu Jul 12th 11:40 AM -- 12:00 PM @ A3
Learning Policy Representations in Multiagent Systems
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A1
Stochastic Variance-Reduced Policy Gradient
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ K1
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ Victoria
Deep Models of Interactions Across Sets
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A9
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A7
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ K11
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A6
Adversarial Regression with Multiple Learners
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A5
End-to-End Learning for the Deep Multivariate Probit Model
Oral
Thu Jul 12th 11:50 AM -- 12:00 PM @ A4
Structured Variationally Auto-encoded Optimization
Break
Thu Jul 12th 12:00 -- 01:30 PM @
Lunch - on your own
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ Victoria
Focused Hierarchical RNNs for Conditional Sequence Processing
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A6
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A7
Composite Functional Gradient Learning of Generative Adversarial Models
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ K11
Improved large-scale graph learning through ridge spectral sparsification
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A1
Investigating Human Priors for Playing Video Games
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A5
Accelerated Spectral Ranking
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A3
Fast Information-theoretic Bayesian Optimisation
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A9
Shampoo: Preconditioned Stochastic Tensor Optimization
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ K1
Learning One Convolutional Layer with Overlapping Patches
Oral
Thu Jul 12th 01:30 -- 01:50 PM @ A4
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Oral
Thu Jul 12th 01:50 -- 02:10 PM @ A3
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A1
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Oral
Thu Jul 12th 01:50 -- 02:10 PM @ K1
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A5
Composite Marginal Likelihood Methods for Random Utility Models
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A6
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A4
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Oral
Thu Jul 12th 01:50 -- 02:10 PM @ K11
Parallel and Streaming Algorithms for K-Core Decomposition
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A9
Characterizing Implicit Bias in Terms of Optimization Geometry
Oral
Thu Jul 12th 01:50 -- 02:10 PM @ Victoria
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Ashwin Kalyan · Stefan Lee · Anitha Kannan · Dhruv Batra
Oral
Thu Jul 12th 01:50 -- 02:00 PM @ A7
Tempered Adversarial Networks
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A1
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A5
Ranking Distributions based on Noisy Sorting
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A9
A Distributed Second-Order Algorithm You Can Trust
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A6
Prediction Rule Reshaping
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A7
Improved Training of Generative Adversarial Networks Using Representative Features
Oral
Thu Jul 12th 02:00 -- 02:10 PM @ A4
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A7
A Two-Step Computation of the Exact GAN Wasserstein Distance
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A4
A Robust Approach to Sequential Information Theoretic Planning
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A5
SQL-Rank: A Listwise Approach to Collaborative Ranking
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A6
Finding Influential Training Samples for Gradient Boosted Decision Trees
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ K11
Fast Approximate Spectral Clustering for Dynamic Networks
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A1
Time Limits in Reinforcement Learning
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A3
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ A9
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ Victoria
Learning long term dependencies via Fourier recurrent units
Oral
Thu Jul 12th 02:10 -- 02:20 PM @ K1
The Multilinear Structure of ReLU Networks
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A4
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A3
Tight Regret Bounds for Bayesian Optimization in One Dimension
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ K1
Understanding the Loss Surface of Neural Networks for Binary Classification
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A6
Noise2Noise: Learning Image Restoration without Clean Data
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A7
Is Generator Conditioning Causally Related to GAN Performance?
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A9
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A5
Extreme Learning to Rank via Low Rank Assumption
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ A1
Visualizing and Understanding Atari Agents
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ Victoria
Training Neural Machines with Trace-Based Supervision
Oral
Thu Jul 12th 02:20 -- 02:30 PM @ K11
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A9
Alternating Randomized Block Coordinate Descent
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ K1
Tropical Geometry of Deep Neural Networks
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ Victoria
Neural Dynamic Programming for Musical Self Similarity
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A4
Robust and Scalable Models of Microbiome Dynamics
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A5
Feasible Arm Identification
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A6
Dimensionality-Driven Learning with Noisy Labels
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ K11
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A7
Black-box Adversarial Attacks with Limited Queries and Information
Oral
Thu Jul 12th 02:30 -- 02:50 PM @ A1
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Oral
Thu Jul 12th 02:30 -- 02:40 PM @ A3
Differentially Private Database Release via Kernel Mean Embeddings
Oral
Thu Jul 12th 02:40 -- 03:00 PM @ A3
To Understand Deep Learning We Need to Understand Kernel Learning
Oral
Thu Jul 12th 02:50 -- 03:10 PM @ A9
Randomized Block Cubic Newton Method
Oral
Thu Jul 12th 02:50 -- 03:00 PM @ K11
Loss Decomposition for Fast Learning in Large Output Spaces
Oral
Thu Jul 12th 02:50 -- 03:00 PM @ Victoria
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Oral
Thu Jul 12th 02:50 -- 03:00 PM @ A6
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Oral
Thu Jul 12th 02:50 -- 03:10 PM @ A1
Smoothed Action Value Functions for Learning Gaussian Policies
Oral
Thu Jul 12th 02:50 -- 03:00 PM @ A4
Stein Variational Message Passing for Continuous Graphical Models
Oral
Thu Jul 12th 02:50 -- 03:10 PM @ K1
A Spline Theory of Deep Learning
Oral
Thu Jul 12th 02:50 -- 03:10 PM @ A5
Bandits with Delayed, Aggregated Anonymous Feedback
Oral
Thu Jul 12th 02:50 -- 03:10 PM @ A7
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Oral
Thu Jul 12th 03:00 -- 03:10 PM @ K11
Ultra Large-Scale Feature Selection using Count-Sketches
Oral
Thu Jul 12th 03:00 -- 03:10 PM @ Victoria
Fast Decoding in Sequence Models Using Discrete Latent Variables
Oral
Thu Jul 12th 03:00 -- 03:10 PM @ A3
Learning in Reproducing Kernel Kreı̆n Spaces
Dino Oglic · Thomas Gaertner
Oral
Thu Jul 12th 03:00 -- 03:10 PM @ A6
Learning to Reweight Examples for Robust Deep Learning
Oral
Thu Jul 12th 03:00 -- 03:10 PM @ A4
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A5
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ K1
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ Victoria
PixelSNAIL: An Improved Autoregressive Generative Model
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A9
Accelerating Greedy Coordinate Descent Methods
Oral
Thu Jul 12th 03:10 -- 03:30 PM @ A3
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A1
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A7
Adversarial Attack on Graph Structured Data
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ K11
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A6
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Oral
Thu Jul 12th 03:10 -- 03:20 PM @ A4
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ K11
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A7
GAIN: Missing Data Imputation using Generative Adversarial Nets
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ K1
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A9
On Acceleration with Noise-Corrupted Gradients
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A4
Bucket Renormalization for Approximate Inference
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A5
Thompson Sampling for Combinatorial Semi-Bandits
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A6
Improving Regression Performance with Distributional Losses
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ A1
Addressing Function Approximation Error in Actor-Critic Methods
Oral
Thu Jul 12th 03:20 -- 03:30 PM @ Victoria
Image Transformer
Break
Thu Jul 12th 03:30 -- 04:00 PM @ Hall B
Coffee Break
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A4
Variational Bayesian dropout: pitfalls and fixes
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A7
The Mechanics of n-Player Differentiable Games
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A5
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ K1
Reviving and Improving Recurrent Back-Propagation
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A1
Configurable Markov Decision Processes
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A3
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A9
Approximate message passing for amplitude based optimization
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ A6
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ K11
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Oral
Thu Jul 12th 04:00 -- 04:20 PM @ Victoria
Using Inherent Structures to design Lean 2-layer RBMs
Abhishek Bansal · Abhinav Anand · Chiranjib Bhattacharyya
Oral
Thu Jul 12th 04:20 -- 04:40 PM @ A1
Beyond the One-Step Greedy Approach in Reinforcement Learning
Oral
Thu Jul 12th 04:20 -- 04:30 PM @ Victoria
Deep Asymmetric Multi-task Feature Learning
Oral
Thu Jul 12th 04:20 -- 04:30 PM @ K11
Approximation Guarantees for Adaptive Sampling
Oral
Thu Jul 12th 04:20 -- 04:40 PM @ A9
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Oral
Thu Jul 12th 04:20 -- 04:40 PM @ A3
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Oral
Thu Jul 12th 04:20 -- 04:30 PM @ A7
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Oral
Thu Jul 12th 04:20 -- 04:30 PM @ K1
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Oral
Thu Jul 12th 04:20 -- 04:30 PM @ A4
Calibrated Estimates of Predictive Uncertainty in Deep Learning
Oral
Thu Jul 12th 04:20 -- 04:40 PM @ A5
Practical Contextual Bandits with Regression Oracles
Oral
Thu Jul 12th 04:20 -- 04:40 PM @ A6
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Oral
Thu Jul 12th 04:30 -- 04:40 PM @ K1
Invariance of Weight Distributions in Rectified MLPs
Oral
Thu Jul 12th 04:30 -- 04:40 PM @ A7
First Order Generative Adversarial Networks
Oral
Thu Jul 12th 04:30 -- 04:40 PM @ A4
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Oral
Thu Jul 12th 04:30 -- 04:40 PM @ Victoria
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Oral
Thu Jul 12th 04:30 -- 04:40 PM @ K11
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A7
Towards Fast Computation of Certified Robustness for ReLU Networks
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ K11
Constrained Interacting Submodular Groupings
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ Victoria
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ K1
Learning Dynamics of Linear Denoising Autoencoders
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A1
Policy and Value Transfer in Lifelong Reinforcement Learning
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A3
Fitting New Speakers Based on a Short Untranscribed Sample
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A9
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A6
Functional Gradient Boosting based on Residual Network Perception
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A5
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Oral
Thu Jul 12th 04:40 -- 04:50 PM @ A4
Scalable approximate Bayesian inference for particle tracking data
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A3
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li · Di He · Fei Tian · Wei Chen · Tao Qin · Liwei Wang · Tie-Yan Liu
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ K11
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A6
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A9
Accelerating Natural Gradient with Higher-Order Invariance
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A7
LaVAN: Localized and Visible Adversarial Noise
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A5
Stochastic Proximal Algorithms for AUC Maximization
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A1
Importance Weighted Transfer of Samples in Reinforcement Learning
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ Victoria
High Performance Zero-Memory Overhead Direct Convolutions
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ K1
Understanding Generalization and Optimization Performance of Deep CNNs
Oral
Thu Jul 12th 04:50 -- 05:00 PM @ A4
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A6
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A5
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi · Ya-Ping Hsieh · Mehmet Fatih Sahin · Volkan Cevher
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A7
Synthesizing Programs for Images using Reinforced Adversarial Learning
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ K1
Composable Planning with Attributes
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ Victoria
ContextNet: Deep learning for Star Galaxy Classification
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A3
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A9
Stochastic Wasserstein Barycenters
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ K11
Representation Tradeoffs for Hyperbolic Embeddings
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A1
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Oral
Thu Jul 12th 05:00 -- 05:20 PM @ A4
Neural Autoregressive Flows
Oral
Thu Jul 12th 05:20 -- 05:40 PM @ A3
Deep Variational Reinforcement Learning for POMDPs
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A6
Open Category Detection with PAC Guarantees
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ Victoria
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Oral
Thu Jul 12th 05:20 -- 05:40 PM @ K1
Measuring abstract reasoning in neural networks
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A5
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A1
Fast Bellman Updates for Robust MDPs
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A7
MAGAN: Aligning Biological Manifolds
Oral
Thu Jul 12th 05:20 -- 05:40 PM @ K11
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A4
Distilling the Posterior in Bayesian Neural Networks
Oral
Thu Jul 12th 05:20 -- 05:30 PM @ A9
Learning Compact Neural Networks with Regularization
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A6
Unbiased Objective Estimation in Predictive Optimization
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ Victoria
Hierarchical Multi-Label Classification Networks
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A9
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A5
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A1
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A4
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Oral
Thu Jul 12th 05:30 -- 05:40 PM @ A7
Max-Mahalanobis Linear Discriminant Analysis Networks
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A5
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A7
Adversarial Time-to-Event Modeling
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A1
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ K1
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A9
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A3
Recurrent Predictive State Policy Networks
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ Victoria
Nonparametric variable importance using an augmented neural network with multi-task learning
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ K11
Local Density Estimation in High Dimensions
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A6
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Oral
Thu Jul 12th 05:40 -- 05:50 PM @ A4
Noisy Natural Gradient as Variational Inference
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A6
Towards Black-box Iterative Machine Teaching
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ K1
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ Victoria
Knowledge Transfer with Jacobian Matching
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A9
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A3
Regret Minimization for Partially Observable Deep Reinforcement Learning
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A1
Decoupling Gradient-Like Learning Rules from Representations
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A7
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ K11
Improving Sign Random Projections With Additional Information
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A5
Learning Localized Spatio-Temporal Models From Streaming Data
Oral
Thu Jul 12th 05:50 -- 06:00 PM @ A4
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Break
Thu Jul 12th 06:15 -- 07:15 PM @ Hall B
Light Evening Snack
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #1
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Richard Zhang · Salar Fattahi · Somayeh Sojoudi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #2
Robust and Scalable Models of Microbiome Dynamics
Travis Gibson · Georg Gerber
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #3
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong LI · Yves Grandvalet · Franck Davoine
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #4
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen · Vijay Badrinarayanan · Chen-Yu Lee · Andrew Rabinovich
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #5
Optimizing the Latent Space of Generative Networks
Piotr Bojanowski · Armand Joulin · David Lopez-Paz · Arthur Szlam
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #6
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Xudong Pan · Mi Zhang · Daizong Ding
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #7
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja · Aurick Zhou · Pieter Abbeel · Sergey Levine
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #8
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Paavo Parmas · Carl E Rasmussen · Jan Peters · Kenji Doya
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #9
Probabilistic Recurrent State-Space Models
Andreas Doerr · Christian Daniel · Martin Schiegg · Duy Nguyen-Tuong · Stefan Schaal · Marc Toussaint · Sebastian Trimpe
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #10
Structured Variationally Auto-encoded Optimization
Xiaoyu Lu · Javier González · Zhenwen Dai · Neil Lawrence
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #11
A Robust Approach to Sequential Information Theoretic Planning
Sue Zheng · Jason Pacheco · John Fisher
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #12
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Miles Lopes · Shusen Wang · Michael Mahoney
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #13
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou · Panayotis Mertikopoulos · Nicholas Bambos · Peter Glynn · Yinyu Ye · Li-Jia Li · Li Fei-Fei
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #14
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Jiaxiang Wu · Weidong Huang · Junzhou Huang · Tong Zhang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #15
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
Ahmed Douik · Babak Hassibi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #16
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles · Philipp Hennig
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #17
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Thomas Dietterich · George Trimponias · Zhitang Chen
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #18
Differentially Private Database Release via Kernel Mean Embeddings
Matej Balog · Ilya Tosltikhin · Bernhard Schölkopf
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #19
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Gail Weiss · Yoav Goldberg · Eran Yahav
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #20
Neural Dynamic Programming for Musical Self Similarity
Christian Walder · Dongwoo Kim
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #21
Learning long term dependencies via Fourier recurrent units
Jiong Zhang · Yibo Lin · Zhao Song · Inderjit Dhillon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #22
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikolaj Binkowski · Gautier Marti · Philippe Donnat
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #23
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Dane Corneil · Wulfram Gerstner · Johanni Brea
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #24
Regret Minimization for Partially Observable Deep Reinforcement Learning
Peter Jin · EECS Kurt Keutzer · Sergey Levine
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #25
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Jiasen Yang · Qiang Liu · Vinayak A Rao · Jennifer Neville
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #26
Unbiased Objective Estimation in Predictive Optimization
Shinji Ito · Akihiro Yabe · Ryohei Fujimaki
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #27
Ultra Large-Scale Feature Selection using Count-Sketches
Amirali Aghazadeh · Ryan Spring · Daniel LeJeune · Gautam Dasarathy · Anshumali Shrivastava · Richard Baraniuk
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #28
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Vladimir Braverman · Stephen Chestnut · Robert Krauthgamer · Yi Li · David Woodruff · Lin Yang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #29
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu · Alexander Irpan · Jacob Andreas · Bobby Kleinberg · Quoc Le · Jon Kleinberg
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #30
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker · Surya Bhupatiraju · Shixiang Gu · Richard E Turner · Zoubin Ghahramani · Sergey Levine
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #31
Composite Marginal Likelihood Methods for Random Utility Models
Zhibing Zhao · Lirong Xia
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #32
Ranking Distributions based on Noisy Sorting
Adil El Mesaoudi-Paul · Eyke Hüllermeier · Robert Busa-Fekete
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #33
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
CMLA Thomas Moreau · Laurent Oudre · CMLA Nicolas Vayatis
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #34
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Zhihao Jia · Sina Lin · Charles Qi · Alex Aiken
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #35
Deep Models of Interactions Across Sets
Jason Hartford · Devon Graham · Kevin Leyton-Brown · Siamak Ravanbakhsh
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #36
ContextNet: Deep learning for Star Galaxy Classification
Noble Kennamer · University of California David Kirkby · Alexander Ihler · University of California Francisco Javier Sanchez-Lopez
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #37
First Order Generative Adversarial Networks
Calvin Seward · Thomas Unterthiner · Urs M Bergmann · Nikolay Jetchev · Sepp Hochreiter
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #38
Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang · Chao Du · Jun Zhu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #39
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Asish Ghoshal · Jean Honorio
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #40
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Alexandre Garcia · Telecom-ParisTech Chloé Clavel · Slim Essid · Florence d'Alche-Buc
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #41
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai · Albert Shaw · Lihong Li · Lin Xiao · Niao He · Zhen Liu · Jianshu Chen · Le Song
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #42
Smoothed Action Value Functions for Learning Gaussian Policies
Ofir Nachum · Mohammad Norouzi · George Tucker · Dale Schuurmans
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #43
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan · Eric Battenberg · Ying Xiao · Yuxuan Wang · Daisy Stanton · Joel Shor · Ron Weiss · Robert Clark · Rif Saurous
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #44
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang · Daisy Stanton · Yu Zhang · RJ-Skerry Ryan · Eric Battenberg · Joel Shor · Ying Xiao · Ye Jia · Fei Ren · Rif Saurous
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #45
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Ahmed M. Alaa Ibrahim · M van der Schaar
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #46
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal · Matt Kusner · Adria Gascon · Varun Kanade
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #47
End-to-End Learning for the Deep Multivariate Probit Model
Di Chen · Yexiang Xue · Carla Gomes
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #48
Differentiable Dynamic Programming for Structured Prediction and Attention
Arthur Mensch · Mathieu Blondel
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #49
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Junhong Lin · Volkan Cevher
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #50
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #51
SQL-Rank: A Listwise Approach to Collaborative Ranking
LIWEI WU · Cho-Jui Hsieh · University of California James Sharpnack
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #52
Extreme Learning to Rank via Low Rank Assumption
Minhao Cheng · Ian Davidson · Cho-Jui Hsieh
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #53
Adversarial Attack on Graph Structured Data
Hanjun Dai · Hui Li · Tian Tian · Xin Huang · Lin Wang · Jun Zhu · Le Song
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #54
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu · Wooyeong Jang · Jiefeng Chen · Lingjiao Chen · Somesh Jha
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #55
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Louis Filstroff · Alberto Lumbreras · Cedric Fevotte
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #56
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Ariel Jaffe · Roi Weiss · Boaz Nadler · Shai Carmi · Yuval Kluger
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #57
Thompson Sampling for Combinatorial Semi-Bandits
Siwei Wang · Wei Chen
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #58
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi · Ya-Ping Hsieh · Mehmet Fatih Sahin · Volkan Cevher
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #59
Deep Asymmetric Multi-task Feature Learning
Hae Beom Lee · Eunho Yang · Sung Ju Hwang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #60
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Ashwin Kalyan · Stefan Lee · Anitha Kannan · Dhruv Batra
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #61
Stein Variational Message Passing for Continuous Graphical Models
Dilin Wang · Zhe Zeng · Qiang Liu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #62
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu · Siddharth Srivastava · Nicholas Hay · Simon Du · Stuart Russell
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #63
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li · Di He · Fei Tian · Wei Chen · Tao Qin · Liwei Wang · Tie-Yan Liu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #64
Fitting New Speakers Based on a Short Untranscribed Sample
Eliya Nachmani · Adam Polyak · Yaniv Taigman · Lior Wolf
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #65
Stochastic Variance-Reduced Policy Gradient
Matteo Papini · Damiano Binaghi · Giuseppe Canonaco · Matteo Pirotta · Marcello Restelli
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #66
Convergent Tree Backup and Retrace with Function Approximation
Ahmed Touati · Pierre-Luc Bacon · Doina Precup · Pascal Vincent
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #67
Alternating Randomized Block Coordinate Descent
Jelena Diakonikolas · Orecchia Lorenzo
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #68
Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta · Tomer Koren · Yoram Singer
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #69
Stochastic Wasserstein Barycenters
Sebastian Claici · Edward Chien · Justin Solomon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #70
Accelerating Natural Gradient with Higher-Order Invariance
Yang Song · Jiaming Song · Stefano Ermon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #71
Learning unknown ODE models with Gaussian processes
Markus Heinonen · Cagatay Yildiz · Henrik Mannerström · Jukka Intosalmi · Harri Lähdesmäki
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #72
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi · Maurizio Filippone
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #73
Fast Decoding in Sequence Models Using Discrete Latent Variables
Lukasz M Kaiser · Samy Bengio · Aurko Roy · Ashish Vaswani · Niki Parmar · Jakob Uszkoreit · Noam Shazeer
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #74
High Performance Zero-Memory Overhead Direct Convolutions
Jiyuan Zhang · Franz Franchetti · Tze Meng Low
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #75
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Shuaiwen Wang · Wenda Zhou · Haihao Lu · Arian Maleki · Vahab Mirrokni
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #76
Improved large-scale graph learning through ridge spectral sparsification
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #77
Distilling the Posterior in Bayesian Neural Networks
Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #78
Scalable approximate Bayesian inference for particle tracking data
Ruoxi Sun · Department of Statistics Liam Paninski
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #79
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Alexey Drutsa
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #80
Practical Contextual Bandits with Regression Oracles
Dylan Foster · Alekh Agarwal · Miroslav Dudik · Haipeng Luo · Robert Schapire
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #81
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou · Pan Xu · Quanquan Gu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #82
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Umut Simsekli · Cagatay Yildiz · Thanh Huy Nguyen · Ali Cemgil · Gaël RICHARD
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #83
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon · James Jordon · Mihaela van der Schaar
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #84
Synthesizing Programs for Images using Reinforced Adversarial Learning
Iaroslav Ganin · Tejas Kulkarni · Igor Babuschkin · S. M. Ali Eslami · Oriol Vinyals
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #85
Geometry Score: A Method For Comparing Generative Adversarial Networks
Valentin Khrulkov · Ivan Oseledets
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #86
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto · Herke van Hoof · David Meger
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #87
Fast Bellman Updates for Robust MDPs
Chin Pang Ho · Marek Petrik · Wolfram Wiesemann
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #88
Configurable Markov Decision Processes
Alberto Maria Metelli · Mirco Mutti · Marcello Restelli
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #89
Prediction Rule Reshaping
Matt Bonakdarpour · Sabyasachi Chatterjee · Rina Barber · John Lafferty
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #90
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma · Yisen Wang · Michael E. Houle · Shuo Zhou · Sarah Erfani · Shutao Xia · Sudanthi Wijewickrema · James Bailey
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #91
Learning Memory Access Patterns
Milad Hashemi · Kevin Swersky · Jamie Smith · Grant Ayers · Heiner Litz · Jichuan Chang · Christos Kozyrakis · Parthasarathy Ranganathan
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #92
Geodesic Convolutional Shape Optimization
Pierre Baque · Edoardo Remelli · Francois Fleuret · EPFL Pascal Fua
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #93
Visualizing and Understanding Atari Agents
Samuel Greydanus · Anurag Koul · Jonathan Dodge · Alan Fern
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #94
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik · Malayandi Palaniappan · Jaime Fisac · Dylan Hadfield-Menell · Stuart Russell · EECS Anca Dragan
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #95
Is Generator Conditioning Causally Related to GAN Performance?
Augustus Odena · Jacob Buckman · Catherine Olsson · Tom B Brown · Christopher Olah · Colin Raffel · Ian Goodfellow
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #96
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Jihun Hamm · Yung-Kyun Noh
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #97
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Vignesh Ganapathiraman · Zhan Shi · Xinhua Zhang · Yaoliang Yu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #98
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu · Gang Niu · Issei Sato · Masashi Sugiyama
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #99
Understanding Generalization and Optimization Performance of Deep CNNs
Pan Zhou · Jiashi Feng
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #100
The Multilinear Structure of ReLU Networks
Thomas Laurent · James von Brecht
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #101
Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari · Silvio Lattanzi · Vahab Mirrokni
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #102
Fast Approximate Spectral Clustering for Dynamic Networks
Lionel Martin · Andreas Loukas · Pierre Vandergheynst
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #103
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Simon Du · Jason Lee · Yuandong Tian · Aarti Singh · Barnabás Póczos
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #104
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Quynh Nguyen · Mahesh Mukkamala · Matthias Hein
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #105
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai · Jeff Bilmes
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #106
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas · Logan Engstrom · Anish Athalye · Jessy Lin
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #107
Using Inherent Structures to design Lean 2-layer RBMs
Abhishek Bansal · Abhinav Anand · Chiranjib Bhattacharyya
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #108
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab · Emanuela Keller · Carl Muroi · David J. Mack · Christian Strässle · Walter Karlen
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #109
Composable Planning with Attributes
Amy Zhang · Sainbayar Sukhbaatar · Adam Lerer · Arthur Szlam · Facebook Rob Fergus
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #110
Measuring abstract reasoning in neural networks
Adam Santoro · Feilx Hill · David GT Barrett · Ari S Morcos · Timothy Lillicrap
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #111
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen · Christopher Harshaw · Hamed Hassani · Amin Karbasi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #112
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Dongwoo Kim · Christian Walder
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #113
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang · Zhengyuan Zhou · Thomas Leung · Li-Jia Li · Li Fei-Fei
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #114
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Daphna Weinshall · Gad A Cohen · Dan Amir
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #115
Composite Functional Gradient Learning of Generative Adversarial Models
Rie Johnson · Tong Zhang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #116
LaVAN: Localized and Visible Adversarial Noise
Danny Karmon · Daniel Zoran · Yoav Goldberg
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #117
Approximation Guarantees for Adaptive Sampling
Eric Balkanski · Yaron Singer
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #118
Constrained Interacting Submodular Groupings
Andrew Cotter · Mahdi Milani Fard · Seungil You · Maya Gupta · Jeff Bilmes
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #119
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Nathan Kallus · Angela Zhou
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #120
Adversarial Regression with Multiple Learners
Liang Tong · Sixie Yu · Scott Alfeld · Yevgeniy Vorobeychik
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #121
Representation Tradeoffs for Hyperbolic Embeddings
Frederic Sala · Chris De Sa · Albert Gu · Christopher Re
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #122
Improving Sign Random Projections With Additional Information
Keegan Kang · Wei Pin Wong
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #123
Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke · Shipra Agrawal · Csaba Szepesvari · Steffen Grünewälder
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #124
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Zeyuan Allen-Zhu · Sebastien Bubeck · Yuanzhi Li
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #125
Learning Policy Representations in Multiagent Systems
Aditya Grover · Maruan Al-Shedivat · Jayesh Gupta · Yura Burda · Harrison Edwards
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #126
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Eugenio Bargiacchi · Timothy Verstraeten · Diederik Roijers · Ann Nowé · Hado van Hasselt
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #127
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu · Aoxiao Zhong · Quanzheng Li · Bin Dong
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #128
Compressing Neural Networks using the Variational Information Bottelneck
Bin Dai · Chen Zhu · Baining Guo · David Wipf
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #129
Scalable Bilinear Pi Learning Using State and Action Features
Yichen Chen · Lihong Li · Mengdi Wang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #130
Time Limits in Reinforcement Learning
Fabio Pardo · Arash Tavakoli · Vitaly Levdik · Petar Kormushev
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #131
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Tal Wagner · Sudipto Guha · Shiva Kasiviswanathan · Nina Mishra
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #132
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Cong Ma · Kaizheng Wang · Yuejie Chi · Yuxin Chen
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #133
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang · Arshdeep Sekhon · Yanjun Qi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #134
Bucket Renormalization for Approximate Inference
Sung-Soo Ahn · Michael Chertkov · Adrian Weller · Jinwoo Shin
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #135
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Takafumi Kajihara · Motonobu Kanagawa · Keisuke Yamazaki · Kenji Fukumizu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #136
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Roberta Raileanu · Emily Denton · Arthur Szlam · Facebook Rob Fergus
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #137
Tropical Geometry of Deep Neural Networks
Liwen Zhang · Gregory Naisat · Lek-Heng Lim
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #138
Learning Dynamics of Linear Denoising Autoencoders
Arnu Pretorius · Steve Kroon · Herman Kamper
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #139
Nonparametric variable importance using an augmented neural network with multi-task learning
Jean Feng · Brian Williamson · Noah Simon · Marco Carone
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #140
Training Neural Machines with Trace-Based Supervision
Matthew Mirman · Dimitar Dimitrov · Pavle Djordjevic · Timon Gehr · Martin Vechev
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #141
Open Category Detection with PAC Guarantees
Si Liu · Risheek Garrepalli · Thomas Dietterich · Alan Fern · Dan Hendrycks
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #142
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
Aaditya Ramdas · Tijana Zrnic · Martin Wainwright · Michael Jordan
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #143
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama · Dave Zachariah · Thomas Schön
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #144
Feasible Arm Identification
Julian Katz-Samuels · Clay Scott
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #145
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Alan Kuhnle · J. Smith · Victoria Crawford · My Thai
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #146
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari · Hamed Hassani · Amin Karbasi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #147
Towards Fast Computation of Certified Robustness for ReLU Networks
Tsui-Wei (Lily) Weng · Huan Zhang · Hongge Chen · Zhao Song · Cho-Jui Hsieh · Luca Daniel · Duane Boning · Inderjit Dhillon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #148
A Two-Step Computation of the Exact GAN Wasserstein Distance
Huidong Liu · Xianfeng GU · Samaras Dimitris
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #149
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Jeremias Knoblauch · Theodoros Damoulas
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #150
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Mingrui Liu · Xiaoxuan Zhang · Zaiyi Chen · Xiaoyu Wang · Tianbao Yang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #151
Calibrated Estimates of Predictive Uncertainty in Deep Learning
Volodymyr Kuleshov · Nathan Fenner · Stefano Ermon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #152
Neural Autoregressive Flows
Chin-Wei Huang · David Krueger · Alexandre Lacoste · Aaron Courville
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #153
Probabilistic Boolean Tensor Decomposition
Tammo Rukat · Christopher Holmes · Christopher Yau
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #154
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Xiao Zhang · Lingxiao Wang · Yaodong Yu · Quanquan Gu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #155
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
Konstantin Mishchenko · Franck Iutzeler · Jérôme Malick · Massih-Reza Amini
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #156
Randomized Block Cubic Newton Method
Nikita Doikov · Abdullah Peter Richtarik
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #157
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
Grigory Yaroslavtsev · Adithya Vadapalli
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #158
Local Density Estimation in High Dimensions
Xian Wu · Moses Charikar · Vishnu Natchu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #159
To Understand Deep Learning We Need to Understand Kernel Learning
Mikhail Belkin · Siyuan Ma · Soumik Mandal
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #160
Learning in Reproducing Kernel Kreı̆n Spaces
Dino Oglic · Thomas Gaertner
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #161
Functional Gradient Boosting based on Residual Network Perception
Atsushi Nitanda · Taiji Suzuki
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #162
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan · Sanmi Koyejo · Kai Zhong · Pradeep Ravikumar
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #163
Characterizing Implicit Bias in Terms of Optimization Geometry
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #164
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher Metzler · Phillip Schniter · Ashok Veeraraghavan · Richard Baraniuk
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #165
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa · Chenyang Tao · Chunyuan Li · Courtney Page · Benjamin Goldstein · Lawrence Carin · Ricardo Henao
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #166
MAGAN: Aligning Biological Manifolds
Matt Amodio · Smita Krishnaswamy
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #167
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Ursula Hebert-Johnson · Michael Kim · Omer Reingold · Guy Rothblum
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #168
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Xueru Zhang · Mohammad Khalili · Mingyan Liu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #169
PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen · Nikhil Mishra · Mostafa Rohaninejad · Pieter Abbeel
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #170
Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Ke · Konrad Zolna · Alessandro Sordoni · MILA Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #171
Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen · Jacob Munkberg · Jon Hasselgren · Samuli Laine · Tero Karras · Miika Aittala · Timo Aila
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #172
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren · Wenyuan Zeng · Bin Yang · Raquel Urtasun
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #173
Policy and Value Transfer in Lifelong Reinforcement Learning
David Abel · Yuu Jinnai · Sophie Guo · George Konidaris · Michael L. Littman
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #174
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Cédric Colas · Olivier Sigaud · Pierre-Yves Oudeyer
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #175
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam Roberts · Jesse Engel · Colin Raffel · Curtis Hawthorne · Douglas Eck
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #176
Understanding the Loss Surface of Neural Networks for Binary Classification
SHIYU LIANG · Ruoyu Sun · Yixuan Li · R Srikant
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #177
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen · Jeffrey Pennington · Samuel Schoenholz
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #178
Reviving and Improving Recurrent Back-Propagation
Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · KiJung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #179
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
Hiroyuki Kasai · Hiroyuki Sato · Bamdev Mishra
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #180
Learning Compact Neural Networks with Regularization
Samet Oymak
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #181
Investigating Human Priors for Playing Video Games
Rachit Dubey · Pulkit Agrawal · Deepak Pathak · Tom Griffiths · Alexei Efros
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #182
Decoupling Gradient-Like Learning Rules from Representations
Philip Thomas · Christoph Dann · Emma Brunskill
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #183
Invariance of Weight Distributions in Rectified MLPs
Susumu Tsuchida · Fred Roosta · Marcus Gallagher
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #184
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Sanjeev Arora · Rong Ge · Behnam Neyshabur · Yi Zhang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #185
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Zengfeng Huang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #186
Loss Decomposition for Fast Learning in Large Output Spaces
En-Hsu Yen · Satyen Kale · Felix Xinnan Yu · Daniel Holtmann-Rice · Sanjiv Kumar · Pradeep Ravikumar
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #187
Stochastic Proximal Algorithms for AUC Maximization
Michael Natole Jr · Yiming Ying · Siwei Lyu
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #188
Accelerated Spectral Ranking
Arpit Agarwal · Prathamesh Patil · Shivani Agarwal
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #189
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg · Jose Hernandez-Lobato · Finale Doshi-Velez · Steffen Udluft
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #190
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan · Didrik Nielsen · Voot Tangkaratt · Wu Lin · Yarin Gal · Akash Srivastava
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #191
Learning One Convolutional Layer with Overlapping Patches
Surbhi Goel · Adam Klivans · Raghu Meka
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #192
A Spline Theory of Deep Learning
Randall Balestriero · Richard Baraniuk
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #193
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Soumya Ghosh · Jiayu Yao · Finale Doshi-Velez
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #194
Variational Bayesian dropout: pitfalls and fixes
Jiri Hron · Alexander Matthews · Zoubin Ghahramani
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #195
Adversarial Learning with Local Coordinate Coding
Jiezhang Cao · Yong Guo · Qingyao Wu · Chunhua Shen · Junzhou Huang · Mingkui Tan
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #196
Learning Representations and Generative Models for 3D Point Clouds
Panagiotis Achlioptas · Olga Diamanti · Ioannis Mitliagkas · Leonidas Guibas
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #197
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Mattias Teye · Hossein Azizpour · Kevin Smith
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #198
Noisy Natural Gradient as Variational Inference
Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #199
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl · Luisa Zintgraf · Tuan Anh Le · Frank Wood · Shimon Whiteson
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #200
Recurrent Predictive State Policy Networks
Ahmed Hefny · Zita Marinho · Wen Sun · Siddhartha Srinivasa · Geoff Gordon
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #201
The Mechanics of n-Player Differentiable Games
David Balduzzi · Sebastien Racaniere · James Martens · Jakob Foerster · Karl Tuyls · Thore Graepel
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #202
Improved Training of Generative Adversarial Networks Using Representative Features
Duhyeon Bang · Hyunjung Shim
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #203
Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann · Ricardo Cerri · Rodrigo Barros
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #204
Knowledge Transfer with Jacobian Matching
Suraj Srinivas · Francois Fleuret
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #205
Towards Black-box Iterative Machine Teaching
Weiyang Liu · Bo Dai · Xingguo Li · Zhen Liu · James Rehg · Le Song
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #206
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja de Balle Pigem · Yu-Xiang Wang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #207
Importance Weighted Transfer of Samples in Reinforcement Learning
Andrea Tirinzoni · Andrea Sessa · Matteo Pirotta · Marcello Restelli
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #208
Beyond the One-Step Greedy Approach in Reinforcement Learning
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #209
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Mark McLeod · Stephen Roberts · Michael A Osborne
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #210
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Wenlong Lyu · Fan Yang · Changhao Yan · Dian Zhou · Xuan Zeng
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #211
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Qiang Sun · Kean Ming Tan · Han Liu · Tong Zhang
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #212
Approximate message passing for amplitude based optimization
Junjie Ma · Ji Xu · Arian Maleki
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #213
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye · Nicholas Carlini · David Wagner
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #214
Tempered Adversarial Networks
Mehdi S. M. Sajjadi · Giambattista Parascandolo · Arash Mehrjou · Bernhard Schölkopf
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #215
Fast Information-theoretic Bayesian Optimisation
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #216
Tight Regret Bounds for Bayesian Optimization in One Dimension
Jonathan Scarlett
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #217
Image Transformer
Niki Parmar · Ashish Vaswani · Jakob Uszkoreit · Lukasz M Kaiser · Noam Shazeer · Alexander Ku · Dustin Tran
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #218
Kernelized Synaptic Weight Matrices
Lorenz Müller · Julien Martel · Giacomo Indiveri
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #219
A Distributed Second-Order Algorithm You Can Trust
Celestine Dünner · Aurelien Lucchi · Matilde Gargiani · An Bian · Thomas Hofmann · Martin Jaggi
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #220
On Acceleration with Noise-Corrupted Gradients
Michael Cohen · Jelena Diakonikolas · Orecchia Lorenzo
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #221
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Netanel Raviv · Rashish Tandon · Alexandros Dimakis · Itzhak Tamo
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #222
Accelerating Greedy Coordinate Descent Methods
Haihao Lu · Robert Freund · Vahab Mirrokni
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #223
Finding Influential Training Samples for Gradient Boosted Decision Trees
Boris Sharchilev · Yury Ustinovskiy · Pavel Serdyukov · Maarten de Rijke
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #224
Improving Regression Performance with Distributional Losses
Ehsan Imani · Martha White
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #225
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid · Mikayel Samvelyan · Christian Schroeder · Gregory Farquhar · Jakob Foerster · Shimon Whiteson
Poster
Thu Jul 12th 06:15 -- 09:00 PM @ Hall B #226
Learning to Act in Decentralized Partially Observable MDPs
Jilles Dibangoye · Olivier Buffet
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ K23
31st International Workshop on Qualitative Reasoning (QR 2018)
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ K24
6th Goal Reasoning Workshop
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ T4
Computer Games Workshop
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ K12
FCA4AI 2018
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ B2
Joint Workshop on AI in Health (day 1)
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ B9
Linguistic and Cognitive Approaches To Dialog Agents (LaCATODA 2018)
Workshop
Fri Jul 13th 08:30 AM -- 12:30 PM @ K22
Tenth International Workshop Modelling and Reasoning in Context (MRC)
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ B3
The 3rd International workshop on biomedical informatics with optimization and machine learning (BOOM)
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ B5
The 3rd International Workshop on Knowledge Discovery in Healthcare Data
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ T3
Towards learning with limited labels: Equivariance, Invariance, and Beyond
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ K2
Fairness, Interpretability, and Explainability Federation of Workshops (day 1)
Workshop
Fri Jul 13th 08:30 AM -- 06:00 PM @ K16
Autonomy in Teams -- Joint Workshop on Sharing Autonomy in Human-Robot Interaction
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ Victoria
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ K1
Solving Partial Assignment Problems using Random Clique Complexes
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A1
RLlib: Abstractions for Distributed Reinforcement Learning
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A3
Learning Registered Point Processes from Idiosyncratic Observations
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A5
Dynamic Regret of Strongly Adaptive Methods
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ K11
Out-of-sample extension of graph adjacency spectral embedding
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A9
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A4
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A7
Mixed batches and symmetric discriminators for GAN training
Oral
Fri Jul 13th 09:30 -- 09:50 AM @ A6
A Reductions Approach to Fair Classification
Oral
Fri Jul 13th 09:50 -- 10:10 AM @ A6
Probably Approximately Metric-Fair Learning
Oral
Fri Jul 13th 09:50 -- 10:10 AM @ A1
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Oral
Fri Jul 13th 09:50 -- 10:10 AM @ A3
Deep Bayesian Nonparametric Tracking
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ A7
Mutual Information Neural Estimation
Oral
Fri Jul 13th 09:50 -- 10:10 AM @ A9
A Progressive Batching L-BFGS Method for Machine Learning
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ K11
Bayesian Model Selection for Change Point Detection and Clustering
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ A5
Online Learning with Abstention
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ A4
Sound Abstraction and Decomposition of Probabilistic Programs
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ Victoria
Learning equations for extrapolation and control
Oral
Fri Jul 13th 09:50 -- 10:00 AM @ K1
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ Victoria
PDE-Net: Learning PDEs from Data
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ K1
Video Prediction with Appearance and Motion Conditions
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ A5
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ A7
Adversarially Regularized Autoencoders
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ A4
Parallel Bayesian Network Structure Learning
Oral
Fri Jul 13th 10:00 -- 10:10 AM @ K11
An Iterative, Sketching-based Framework for Ridge Regression
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ K1
Neural Program Synthesis from Diverse Demonstration Videos
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A1
Mix & Match - Agent Curricula for Reinforcement Learning
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A4
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A9
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A3
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A7
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ K11
Provable Variable Selection for Streaming Features
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A5
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ Victoria
Transformation Autoregressive Networks
Oral
Fri Jul 13th 10:10 -- 10:20 AM @ A6
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A4
Temporal Poisson Square Root Graphical Models
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A5
Firing Bandits: Optimizing Crowdfunding
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A9
Estimation of Markov Chain via Rank-constrained Likelihood
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ Victoria
Weightless: Lossy weight encoding for deep neural network compression
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A6
Blind Justice: Fairness with Encrypted Sensitive Attributes
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ K11
Learning Low-Dimensional Temporal Representations
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A1
Learning to Explore via Meta-Policy Gradient
Oral
Fri Jul 13th 10:20 -- 10:30 AM @ A7
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Break
Fri Jul 13th 10:30 -- 11:00 AM @ Hall B
Coffee Break
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A1
Hierarchical Imitation and Reinforcement Learning
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ Victoria
Efficient Neural Audio Synthesis
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ K1
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ K11
Competitive Caching with Machine Learned Advice
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A5
Online Linear Quadratic Control
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A3
Learning Adversarially Fair and Transferable Representations
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A7
Junction Tree Variational Autoencoder for Molecular Graph Generation
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A4
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A6
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Oral
Fri Jul 13th 11:00 -- 11:20 AM @ A9
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Oral
Fri Jul 13th 11:20 -- 11:30 AM @ A3
Learning Semantic Representations for Unsupervised Domain Adaptation
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ A9
Frank-Wolfe with Subsampling Oracle
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ A1
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Oral
Fri Jul 13th 11:20 -- 11:30 AM @ A6
Improved nearest neighbor search using auxiliary information and priority functions
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ Victoria
Understanding and Simplifying One-Shot Architecture Search
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ A5
Semiparametric Contextual Bandits
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ A7
Semi-Amortized Variational Autoencoders
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ K1
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Oral
Fri Jul 13th 11:20 -- 11:30 AM @ K11
Distributed Clustering via LSH Based Data Partitioning
Oral
Fri Jul 13th 11:20 -- 11:40 AM @ A4
State Space Gaussian Processes with Non-Gaussian Likelihood
Oral
Fri Jul 13th 11:30 -- 11:40 AM @ K11
Learning to Branch
Oral
Fri Jul 13th 11:30 -- 11:40 AM @ A6
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Oral
Fri Jul 13th 11:30 -- 11:40 AM @ A3
Adapting Images and Representations with Domain Adversarial Learning
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ K1
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ K11
Compiling Combinatorial Prediction Games
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ Victoria
Path-Level Network Transformation for Efficient Architecture Search
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A9
On Matching Pursuit and Coordinate Descent
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A7
Iterative Amortized Inference
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A4
Constant-Time Predictive Distributions for Gaussian Processes
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A1
State Abstractions for Lifelong Reinforcement Learning
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A5
Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A6
Topological mixture estimation
Oral
Fri Jul 13th 11:40 -- 11:50 AM @ A3
Rectify Heterogeneous Models with Semantic Mapping
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A1
Policy Optimization with Demonstrations
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A4
Large-Scale Cox Process Inference using Variational Fourier Features
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A5
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A6
Revealing Common Statistical Behaviors in Heterogeneous Populations
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ K11
Approximation Algorithms for Cascading Prediction Models
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ K1
Optimization Landscape and Expressivity of Deep CNNs
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ Victoria
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A9
Adaptive Three Operator Splitting
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A3
Detecting and Correcting for Label Shift with Black Box Predictors
Oral
Fri Jul 13th 11:50 AM -- 12:00 PM @ A7
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Break
Fri Jul 13th 12:00 -- 01:30 PM @
Lunch - on your own
Invited Talk
Fri Jul 13th 01:30 -- 02:30 PM @ A1
Language to Action: towards Interactive Task Learning with Physical Agents
Joyce Chai
Workshop
Fri Jul 13th 02:00 -- 06:00 PM @ K22
Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge (day 1)
Invited Talk
Fri Jul 13th 02:30 -- 03:30 PM @ A1
Building Machines that Learn and Think Like People
Josh Tenenbaum
Break
Fri Jul 13th 03:30 -- 04:00 PM @ Hall B
Coffee Break
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A3
Analyzing Uncertainty in Neural Machine Translation
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A1
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A4
Stein Variational Gradient Descent Without Gradient
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A6
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A9
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ K1
Efficient end-to-end learning for quantizable representations
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A7
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ Victoria
Progress & Compress: A scalable framework for continual learning
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ K11
On the Spectrum of Random Features Maps of High Dimensional Data
Oral
Fri Jul 13th 04:00 -- 04:20 PM @ A5
Causal Bandits with Propagating Inference
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ K11
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Oral
Fri Jul 13th 04:20 -- 04:40 PM @ Victoria
Overcoming Catastrophic Forgetting with Hard Attention to the Task
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ A1
Policy Optimization as Wasserstein Gradient Flows
Oral
Fri Jul 13th 04:20 -- 04:40 PM @ A5
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ A9
Level-Set Methods for Finite-Sum Constrained Convex Optimization
Oral
Fri Jul 13th 04:20 -- 04:40 PM @ A7
Autoregressive Quantile Networks for Generative Modeling
Oral
Fri Jul 13th 04:20 -- 04:40 PM @ A4
Minibatch Gibbs Sampling on Large Graphical Models
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ A6
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ A3
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Oral
Fri Jul 13th 04:20 -- 04:30 PM @ K1
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ A3
Adaptive Sampled Softmax with Kernel Based Sampling
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ K1
A Boo(n) for Evaluating Architecture Performance
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ A9
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ A6
Attention-based Deep Multiple Instance Learning
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ A1
Clipped Action Policy Gradient
Oral
Fri Jul 13th 04:30 -- 04:40 PM @ K11
Spectrally Approximating Large Graphs with Smaller Graphs
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A9
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A6
Learning and Memorization
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A7
Stochastic Video Generation with a Learned Prior
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A5
Budgeted Experiment Design for Causal Structure Learning
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A4
On Nesting Monte Carlo Estimators
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A1
Fourier Policy Gradients
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ A3
Hierarchical Text Generation and Planning for Strategic Dialogue
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ Victoria
Rapid Adaptation with Conditionally Shifted Neurons
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ K1
Entropy-SGD optimizes the prior of a PAC-Bayes bound
Oral
Fri Jul 13th 04:40 -- 04:50 PM @ K11
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A1
Self-Imitation Learning
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ K11
Rates of Convergence of Spectral Methods for Graphon Estimation
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ K1
On the Limitations of First-Order Approximation in GAN Dynamics
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ Victoria
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A4
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A6
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A7
Disentangled Sequential Autoencoder
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A9
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Oral
Fri Jul 13th 04:50 -- 05:00 PM @ A5
The Hierarchical Adaptive Forgetting Variational Filter
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ K1
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ Victoria
WSNet: Compact and Efficient Networks Through Weight Sampling
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A9
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A6
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ K11
Convolutional Imputation of Matrix Networks
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A3
The Hidden Vulnerability of Distributed Learning in Byzantium
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A1
Mean Field Multi-Agent Reinforcement Learning
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A5
Orthogonal Machine Learning: Power and Limitations
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A7
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Oral
Fri Jul 13th 05:00 -- 05:20 PM @ A4
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ A6
Classification from Pairwise Similarity and Unlabeled Data
Oral
Fri Jul 13th 05:20 -- 05:40 PM @ Victoria
StrassenNets: Deep Learning with a Multiplication Budget
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ K1
Bounding and Counting Linear Regions of Deep Neural Networks
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ K11
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ A4
CRVI: Convex Relaxation for Variational Inference
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ A9
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Oral
Fri Jul 13th 05:20 -- 05:40 PM @ A5
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Oral
Fri Jul 13th 05:20 -- 05:40 PM @ A3
Asynchronous Byzantine Machine Learning (the case of SGD)
Oral
Fri Jul 13th 05:20 -- 05:40 PM @ A1
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Oral
Fri Jul 13th 05:20 -- 05:30 PM @ A7
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ A6
Comparison-Based Random Forests
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ A4
Stein Points
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ A9
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ A7
Noisin: Unbiased Regularization for Recurrent Neural Networks
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ K1
Bounds on the Approximation Power of Feedforward Neural Networks
Oral
Fri Jul 13th 05:30 -- 05:40 PM @ K11
On the Implicit Bias of Dropout
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A5
Accurate Inference for Adaptive Linear Models
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A4
Message Passing Stein Variational Gradient Descent
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A6
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ K11
A Unified Framework for Structured Low-rank Matrix Learning
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A7
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A3
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ A9
Efficient First-Order Algorithms for Adaptive Signal Denoising
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ Victoria
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Oral
Fri Jul 13th 05:40 -- 05:50 PM @ K1
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A3
Communication-Computation Efficient Gradient Coding
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A1
The Uncertainty Bellman Equation and Exploration
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ K1
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ K11
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ Victoria
Born Again Neural Networks
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A5
Detecting non-causal artifacts in multivariate linear regression models
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A4
Pathwise Derivatives Beyond the Reparameterization Trick
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A6
Active Learning with Logged Data
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A9
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
Oral
Fri Jul 13th 05:50 -- 06:00 PM @ A7
Inter and Intra Topic Structure Learning with Word Embeddings
Break
Fri Jul 13th 06:15 -- 07:15 PM @ Hall B
Light Evening Snack
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #1
Stein Points
Wilson Ye Chen · Lester Mackey · Jackson Gorham · Francois-Xavier Briol · Chris J Oates
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #2
Large-Scale Cox Process Inference using Variational Fourier Features
ST John · James Hensman
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #3
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Zaiyi Chen · Yi Xu · Enhong Chen · Tianbao Yang
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #4
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Mingyi Hong · Meisam Razaviyayn · Jason Lee
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #5
A Progressive Batching L-BFGS Method for Machine Learning
Vijaya Raghavendra Bollapragada · Jorge Nocedal · Dheevatsa Mudigere · Hao-Jun M Shi · Peter Tang
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #6
WSNet: Compact and Efficient Networks Through Weight Sampling
Xiaojie Jin · Yingzhen Yang · Ning Xu · Jianchao Yang · Nebojsa Jojic · Jiashi Feng · Shuicheng Yan
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #7
Entropy-SGD optimizes the prior of a PAC-Bayes bound
Gintare Karolina Dziugaite · Daniel Roy
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #8
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce · Alexandra Brintrup · Mohamed Zaki · Andy Neely
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #9
Competitive Caching with Machine Learned Advice
Thodoris Lykouris · Sergei Vassilvitskii
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #10
Approximation Algorithms for Cascading Prediction Models
Matthew Streeter
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #11
Orthogonal Machine Learning: Power and Limitations
Ilias Zadik · Lester Mackey · Vasilis Syrgkanis
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #12
Causal Bandits with Propagating Inference
Akihiro Yabe · Daisuke Hatano · Hanna Sumita · Shinji Ito · Naonori Kakimura · Takuro Fukunaga · Ken-ichi Kawarabayashi
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #13
Mix & Match - Agent Curricula for Reinforcement Learning
Wojciech Czarnecki · Siddhant Jayakumar · Max Jaderberg · Leonard Hasenclever · Yee Teh · Nicolas Heess · Simon Osindero · Razvan Pascanu
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #14
The Uncertainty Bellman Equation and Exploration
Brendan O'Donoghue · Ian Osband · Remi Munos · Vlad Mnih
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #15
Hierarchical Imitation and Reinforcement Learning
Hoang M Le · Nan Jiang · Alekh Agarwal · Miroslav Dudik · Yisong Yue · Hal Daume
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #16
Policy Optimization with Demonstrations
Bingyi Kang · Zequn Jie · Jiashi Feng
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #17
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Arman Sharifi Kolarijani · Peyman Mohajerin Esfahani · Tamas Keviczky
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #18
Level-Set Methods for Finite-Sum Constrained Convex Optimization
Qihang Lin · Runchao Ma · Tianbao Yang
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #19
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Weili Nie · Yang Zhang · Ankit Patel
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #20
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar · Rudolf Kadlec · Jan Kleindienst
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #21
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang · Richard Liaw · Robert Nishihara · Philipp Moritz · Roy Fox · Ken Goldberg · Joseph Gonzalez · Michael Jordan · Ion Stoica
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #22
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel · Rong Ge · Sham Kakade · Mehran Mesbahi
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #23
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
Hao Lu · Yuan Cao · Junwei Lu · Han Liu · Zhaoran Wang
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #24
Sound Abstraction and Decomposition of Probabilistic Programs
Steven Holtzen · Guy Van den Broeck · Todd Millstein
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #25
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aäron van den Oord · Yazhe Li · Igor Babuschkin · Karen Simonyan · Oriol Vinyals · koray kavukcuoglu · George van den Driessche · Edward Lockhart · Luis C Cobo · Florian Stimberg · Norman Casagrande · Dominik Grewe · Seb Noury · Sander Dieleman · Erich Elsen · Nal Kalchbrenner · Heiga Zen · Alex Graves · Helen King · Tom Walters · Dan Belov · Demis Hassabis
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #26
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar · Aditya Grover · Stefano Ermon
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #27
Revealing Common Statistical Behaviors in Heterogeneous Populations
Andrey Zhitnikov · Rotem Mulayoff · Tomer Michaeli
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #28
Improved nearest neighbor search using auxiliary information and priority functions
Omid Keivani · Kaushik Sinha
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #29
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
Aviral Kumar · Sunita Sarawagi · Ujjwal Jain
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #30
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Giacomo Boracchi · Diego Carrera · Cristiano Cervellera · Danilo Macciò
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #31
An Iterative, Sketching-based Framework for Ridge Regression
Agniva Chowdhury · Jiasen Yang · Petros Drineas
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #32
Learning Low-Dimensional Temporal Representations
Bing Su · Ying Wu
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #33
Rapid Adaptation with Conditionally Shifted Neurons
Tsendsuren Munkhdalai · Xingdi Yuan · Soroush Mehri · Adam Trischler
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #34
PDE-Net: Learning PDEs from Data
Zichao Long · Yiping Lu · Xianzhong Ma · Bin Dong
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #35
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Manolis Tsakiris · Rene Vidal
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #36
Topological mixture estimation
Steve Huntsman
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #37
On Matching Pursuit and Coordinate Descent
Francesco Locatello · Anant Raj · Sai Praneeth Reddy Karimireddy · Gunnar Raetsch · Bernhard Schölkopf · Sebastian Stich · Martin Jaggi
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #38
Frank-Wolfe with Subsampling Oracle
Thomas Kerdreux · Fabian Pedregosa · Alexandre d'Aspremont
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #39
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Yangchen Pan · Amir-massoud Farahmand · Martha White · Saleh Nabi · Piyush Grover · Daniel Nikovski
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #40
Fourier Policy Gradients
Matthew Fellows · Kamil Ciosek · Shimon Whiteson
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #41
Adaptive Three Operator Splitting
Fabian Pedregosa · Gauthier Gidel
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #42
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Alp Yurtsever · Olivier Fercoq · Francesco Locatello · Volkan Cevher
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #43
Learning Semantic Representations for Unsupervised Domain Adaptation
Shaoan Xie · Zibin Zheng · Liang Chen · Chuan Chen
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #44
Learning Adversarially Fair and Transferable Representations
David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #45
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran · Ohad Shamir
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #46
Efficient end-to-end learning for quantizable representations
Yeonwoo Jeong · Hyun Oh Song
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #47
Solving Partial Assignment Problems using Random Clique Complexes
Charu Sharma · Deepak Nathani · Manu Kaul
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #48
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Siyuan Qi · Baoxiong Jia · Song-Chun Zhu
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #49
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Koulik Khamaru · Martin Wainwright
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #50
Estimation of Markov Chain via Rank-constrained Likelihood
XUDONG LI · Mengdi Wang · Anru Zhang
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #51
Efficient First-Order Algorithms for Adaptive Signal Denoising
Dmitrii Ostrovskii · Zaid Harchaoui
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #52
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Pan Xu · Tianhao Wang · Quanquan Gu
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #53
Noisin: Unbiased Regularization for Recurrent Neural Networks
Adji Bousso Dieng · Rajesh Ranganath · Jaan Altosaar · David Blei
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #54
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Zhengping Che · Sanjay Purushotham · Max Guangyu Li · Bo Jiang · Yan Liu
Poster
Fri Jul 13th 06:15 -- 09:00 PM @ Hall B #55
Disentangled Sequential Autoencoder
Yingzhen Li · Stephan Mandt
Poster
Fri Jul 13th 06:1