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Session
Thu Jul 12 01:00 AM -- 01:20 AM (PDT) @ A1
Best Paper Session 2
Oral
Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A9
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A6
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A7
Geometry Score: A Method For Comparing Generative Adversarial Networks
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A3
Learning Policy Representations in Multiagent Systems
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A1
Convergent Tree Backup and Retrace with Function Approximation
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ K11
Probabilistic Boolean Tensor Decomposition
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A4
Learning unknown ODE models with Gaussian processes
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ A5
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ K1
Learning Memory Access Patterns
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Thu Jul 12 02:00 AM -- 02:20 AM (PDT) @ Victoria
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
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Thu Jul 12 02:20 AM -- 02:30 AM (PDT) @ A7
Optimizing the Latent Space of Generative Networks
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Thu Jul 12 02:20 AM -- 02:30 AM (PDT) @ A3
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ A9
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ A1
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ K1
Geodesic Convolutional Shape Optimization
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ Victoria
Compressing Neural Networks using the Variational Information Bottelneck
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Thu Jul 12 02:20 AM -- 02:30 AM (PDT) @ K11
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ A4
Constraining the Dynamics of Deep Probabilistic Models
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Thu Jul 12 02:20 AM -- 02:40 AM (PDT) @ A5
Differentiable Dynamic Programming for Structured Prediction and Attention
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Thu Jul 12 02:20 AM -- 02:30 AM (PDT) @ A6
Residual Unfairness in Fair Machine Learning from Prejudiced Data
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Thu Jul 12 02:30 AM -- 02:40 AM (PDT) @ A7
Adversarial Learning with Local Coordinate Coding
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Thu Jul 12 02:30 AM -- 02:40 AM (PDT) @ A6
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
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Thu Jul 12 02:30 AM -- 02:40 AM (PDT) @ A3
Learning to Act in Decentralized Partially Observable MDPs
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Thu Jul 12 02:30 AM -- 02:40 AM (PDT) @ K11
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A3
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A7
Learning Representations and Generative Models for 3D Point Clouds
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A5
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A1
Scalable Bilinear Pi Learning Using State and Action Features
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A6
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A4
Probabilistic Recurrent State-Space Models
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ A9
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ K11
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
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Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ K1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A9
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ K1
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A7
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ Victoria
Deep Models of Interactions Across Sets
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A6
Adversarial Regression with Multiple Learners
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ K11
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A5
End-to-End Learning for the Deep Multivariate Probit Model
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A4
Structured Variationally Auto-encoded Optimization
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A1
Stochastic Variance-Reduced Policy Gradient
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Thu Jul 12 02:50 AM -- 03:00 AM (PDT) @ A3
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ K1
Learning One Convolutional Layer with Overlapping Patches
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A5
Accelerated Spectral Ranking
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A3
Fast Information-theoretic Bayesian Optimisation
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ Victoria
Focused Hierarchical RNNs for Conditional Sequence Processing
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ K11
Improved large-scale graph learning through ridge spectral sparsification
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A9
Shampoo: Preconditioned Stochastic Tensor Optimization
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A7
Composite Functional Gradient Learning of Generative Adversarial Models
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A4
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A6
Inductive Two-Layer Modeling with Parametric Bregman Transfer
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Thu Jul 12 04:30 AM -- 04:50 AM (PDT) @ A1
Investigating Human Priors for Playing Video Games
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A5
Composite Marginal Likelihood Methods for Random Utility Models
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A4
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
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Thu Jul 12 04:50 AM -- 05:10 AM (PDT) @ K11
Parallel and Streaming Algorithms for K-Core Decomposition
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A7
Tempered Adversarial Networks
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Thu Jul 12 04:50 AM -- 05:10 AM (PDT) @ Victoria
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
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Thu Jul 12 04:50 AM -- 05:10 AM (PDT) @ K1
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A9
Characterizing Implicit Bias in Terms of Optimization Geometry
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Thu Jul 12 04:50 AM -- 05:10 AM (PDT) @ A3
Optimization, fast and slow: optimally switching between local and Bayesian optimization
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A1
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
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Thu Jul 12 04:50 AM -- 05:00 AM (PDT) @ A6
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A6
Prediction Rule Reshaping
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A9
A Distributed Second-Order Algorithm You Can Trust
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A7
Improved Training of Generative Adversarial Networks Using Representative Features
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A4
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A5
Ranking Distributions based on Noisy Sorting
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Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A1
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A7
A Two-Step Computation of the Exact GAN Wasserstein Distance
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A6
Finding Influential Training Samples for Gradient Boosted Decision Trees
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ K11
Fast Approximate Spectral Clustering for Dynamic Networks
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A4
A Robust Approach to Sequential Information Theoretic Planning
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A5
SQL-Rank: A Listwise Approach to Collaborative Ranking
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A9
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A3
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ K1
The Multilinear Structure of ReLU Networks
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ A1
Time Limits in Reinforcement Learning
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Thu Jul 12 05:10 AM -- 05:20 AM (PDT) @ Victoria
Learning long term dependencies via Fourier recurrent units
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A7
Is Generator Conditioning Causally Related to GAN Performance?
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A1
Visualizing and Understanding Atari Agents
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A6
Noise2Noise: Learning Image Restoration without Clean Data
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ K1
Understanding the Loss Surface of Neural Networks for Binary Classification
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A5
Extreme Learning to Rank via Low Rank Assumption
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A4
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A9
Gradient Coding from Cyclic MDS Codes and Expander Graphs
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ K11
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ Victoria
Training Neural Machines with Trace-Based Supervision
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Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A3
Tight Regret Bounds for Bayesian Optimization in One Dimension
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ K1
Tropical Geometry of Deep Neural Networks
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A3
To Understand Deep Learning We Need to Understand Kernel Learning
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ K11
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A5
Feasible Arm Identification
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A1
The Mirage of Action-Dependent Baselines in Reinforcement Learning
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A4
Robust and Scalable Models of Microbiome Dynamics
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A6
Dimensionality-Driven Learning with Noisy Labels
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A7
Black-box Adversarial Attacks with Limited Queries and Information
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ A9
Alternating Randomized Block Coordinate Descent
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Thu Jul 12 05:30 AM -- 05:50 AM (PDT) @ Victoria
Neural Dynamic Programming for Musical Self Similarity
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Thu Jul 12 05:50 AM -- 06:10 AM (PDT) @ A7
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
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Thu Jul 12 05:50 AM -- 06:10 AM (PDT) @ A5
Bandits with Delayed, Aggregated Anonymous Feedback
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Thu Jul 12 05:50 AM -- 06:00 AM (PDT) @ Victoria
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
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Thu Jul 12 05:50 AM -- 06:10 AM (PDT) @ K1
A Spline Theory of Deep Learning
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Thu Jul 12 05:50 AM -- 06:10 AM (PDT) @ A1
Smoothed Action Value Functions for Learning Gaussian Policies
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Thu Jul 12 05:50 AM -- 06:00 AM (PDT) @ K11
Loss Decomposition for Fast Learning in Large Output Spaces
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Thu Jul 12 05:50 AM -- 06:10 AM (PDT) @ A3
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
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Thu Jul 12 05:50 AM -- 06:00 AM (PDT) @ A4
Stein Variational Message Passing for Continuous Graphical Models
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Thu Jul 12 05:50 AM -- 06:00 AM (PDT) @ A6
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
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Thu Jul 12 06:00 AM -- 06:10 AM (PDT) @ K11
Ultra Large-Scale Feature Selection using Count-Sketches
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Thu Jul 12 06:00 AM -- 06:10 AM (PDT) @ Victoria
Fast Decoding in Sequence Models Using Discrete Latent Variables
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Thu Jul 12 06:00 AM -- 06:10 AM (PDT) @ A6
Learning to Reweight Examples for Robust Deep Learning
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Thu Jul 12 06:00 AM -- 06:10 AM (PDT) @ A4
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A5
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A4
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A1
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A6
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ Victoria
PixelSNAIL: An Improved Autoregressive Generative Model
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ K11
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A9
Accelerating Greedy Coordinate Descent Methods
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ A3
Differentially Private Database Release via Kernel Mean Embeddings
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Thu Jul 12 06:10 AM -- 06:20 AM (PDT) @ K1
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A6
Improving Regression Performance with Distributional Losses
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ K11
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A4
Bucket Renormalization for Approximate Inference
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A1
Addressing Function Approximation Error in Actor-Critic Methods
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ K1
Stronger Generalization Bounds for Deep Nets via a Compression Approach
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A9
On Acceleration with Noise-Corrupted Gradients
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A5
Thompson Sampling for Combinatorial Semi-Bandits
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A7
GAIN: Missing Data Imputation using Generative Adversarial Nets
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Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A3
Learning in Reproducing Kernel Kreı̆n Spaces
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ K11
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A4
Variational Bayesian dropout: pitfalls and fixes
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ Victoria
Using Inherent Structures to design Lean 2-layer RBMs
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A5
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A7
The Mechanics of n-Player Differentiable Games
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A6
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A3
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ A9
Approximate message passing for amplitude based optimization
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Thu Jul 12 07:00 AM -- 07:20 AM (PDT) @ K1
Reviving and Improving Recurrent Back-Propagation
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Thu Jul 12 07:20 AM -- 07:30 AM (PDT) @ A4
Accurate Uncertainties for Deep Learning Using Calibrated Regression
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Thu Jul 12 07:20 AM -- 07:30 AM (PDT) @ K1
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
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Thu Jul 12 07:20 AM -- 07:40 AM (PDT) @ A5
Practical Contextual Bandits with Regression Oracles
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Thu Jul 12 07:20 AM -- 07:40 AM (PDT) @ A3
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
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Thu Jul 12 07:20 AM -- 07:30 AM (PDT) @ K11
Approximation Guarantees for Adaptive Sampling
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Thu Jul 12 07:20 AM -- 07:40 AM (PDT) @ A9
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
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Thu Jul 12 07:20 AM -- 07:40 AM (PDT) @ A6
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
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Thu Jul 12 07:20 AM -- 07:30 AM (PDT) @ A7
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
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Thu Jul 12 07:20 AM -- 07:40 AM (PDT) @ A1
Beyond the One-Step Greedy Approach in Reinforcement Learning
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Thu Jul 12 07:20 AM -- 07:30 AM (PDT) @ Victoria
Deep Asymmetric Multi-task Feature Learning
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Thu Jul 12 07:30 AM -- 07:40 AM (PDT) @ K11
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
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Thu Jul 12 07:30 AM -- 07:40 AM (PDT) @ A4
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
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Thu Jul 12 07:30 AM -- 07:40 AM (PDT) @ K1
Invariance of Weight Distributions in Rectified MLPs
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Thu Jul 12 07:30 AM -- 07:40 AM (PDT) @ Victoria
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ K1
Learning Dynamics of Linear Denoising Autoencoders
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A6
Functional Gradient Boosting based on Residual Network Perception
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ Victoria
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A9
prDeep: Robust Phase Retrieval with a Flexible Deep Network
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A4
Scalable approximate Bayesian inference for particle tracking data
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ K11
Constrained Interacting Submodular Groupings
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A7
Towards Fast Computation of Certified Robustness for ReLU Networks
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A1
Policy and Value Transfer in Lifelong Reinforcement Learning
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A5
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
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Thu Jul 12 07:40 AM -- 07:50 AM (PDT) @ A3
Fitting New Speakers Based on a Short Untranscribed Sample
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A6
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ K11
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A3
Towards Binary-Valued Gates for Robust LSTM Training
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A9
Accelerating Natural Gradient with Higher-Order Invariance
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ Victoria
High Performance Zero-Memory Overhead Direct Convolutions
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ K1
Understanding Generalization and Optimization Performance of Deep CNNs
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A1
Importance Weighted Transfer of Samples in Reinforcement Learning
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A5
Stochastic Proximal Algorithms for AUC Maximization
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A7
LaVAN: Localized and Visible Adversarial Noise
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Thu Jul 12 07:50 AM -- 08:00 AM (PDT) @ A4
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ A3
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ K11
Representation Tradeoffs for Hyperbolic Embeddings
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ A1
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ K1
Composable Planning with Attributes
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ A5
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ A6
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ A7
Synthesizing Programs for Images using Reinforced Adversarial Learning
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Thu Jul 12 08:00 AM -- 08:20 AM (PDT) @ Victoria
ContextNet: Deep learning for Star Galaxy Classification
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Thu Jul 12 08:20 AM -- 08:30 AM (PDT) @ A4
Distilling the Posterior in Bayesian Neural Networks
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Thu Jul 12 08:20 AM -- 08:40 AM (PDT) @ K1
Measuring abstract reasoning in neural networks
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Thu Jul 12 08:20 AM -- 08:30 AM (PDT) @ A9
Learning Compact Neural Networks with Regularization
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Thu Jul 12 08:20 AM -- 08:30 AM (PDT) @ Victoria
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
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Thu Jul 12 08:20 AM -- 08:40 AM (PDT) @ A3
Deep Variational Reinforcement Learning for POMDPs
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Thu Jul 12 08:20 AM -- 08:40 AM (PDT) @ K11
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
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Thu Jul 12 08:20 AM -- 08:30 AM (PDT) @ A5
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
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Thu Jul 12 08:20 AM -- 08:30 AM (PDT) @ A6
Open Category Detection with PAC Guarantees
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Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A5
Self-Bounded Prediction Suffix Tree via Approximate String Matching
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Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A7
Max-Mahalanobis Linear Discriminant Analysis Networks
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Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A9
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
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Oral
Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A6
Unbiased Objective Estimation in Predictive Optimization
Oral
Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ Victoria
Hierarchical Multi-Label Classification Networks
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Oral
Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A1
Decoupling Gradient-Like Learning Rules from Representations
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PDF]
Oral
Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A4
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ K1
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ Victoria
Nonparametric variable importance using an augmented neural network with multi-task learning
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Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A5
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A9
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
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PDF]
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A3
Recurrent Predictive State Policy Networks
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A4
Noisy Natural Gradient as Variational Inference
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A1
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
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PDF]
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ A6
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Oral
Thu Jul 12 08:40 AM -- 08:50 AM (PDT) @ K11
Local Density Estimation in High Dimensions
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ Victoria
Knowledge Transfer with Jacobian Matching
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Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A3
Regret Minimization for Partially Observable Deep Reinforcement Learning
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A6
Towards Black-box Iterative Machine Teaching
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A9
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
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Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A4
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
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Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ K11
Improving Sign Random Projections With Additional Information
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ K1
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A5
Learning Localized Spatio-Temporal Models From Streaming Data
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A7
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
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PDF]
Oral
Thu Jul 12 08:50 AM -- 09:00 AM (PDT) @ A1
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #1
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #2
Robust and Scalable Models of Microbiome Dynamics
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #3
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #4
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #5
Optimizing the Latent Space of Generative Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #6
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #7
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #8
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
In
Posters Thu
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #9
Probabilistic Recurrent State-Space Models
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #10
Structured Variationally Auto-encoded Optimization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #11
A Robust Approach to Sequential Information Theoretic Planning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #12
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #13
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #14
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #15
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #16
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #17
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #18
Differentially Private Database Release via Kernel Mean Embeddings
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #19
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #20
Neural Dynamic Programming for Musical Self Similarity
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #21
Learning long term dependencies via Fourier recurrent units
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #22
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #23
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #24
Regret Minimization for Partially Observable Deep Reinforcement Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #25
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
In
Posters Thu
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #26
Unbiased Objective Estimation in Predictive Optimization
In
Posters Thu
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #27
Ultra Large-Scale Feature Selection using Count-Sketches
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #28
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #29
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #30
The Mirage of Action-Dependent Baselines in Reinforcement Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #31
Composite Marginal Likelihood Methods for Random Utility Models
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #32
Ranking Distributions based on Noisy Sorting
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #33
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #34
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #35
Deep Models of Interactions Across Sets
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #36
ContextNet: Deep learning for Star Galaxy Classification
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #37
First Order Generative Adversarial Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #38
Max-Mahalanobis Linear Discriminant Analysis Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #39
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #40
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #41
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #42
Smoothed Action Value Functions for Learning Gaussian Policies
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #43
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #44
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #45
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #46
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #47
End-to-End Learning for the Deep Multivariate Probit Model
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #48
Differentiable Dynamic Programming for Structured Prediction and Attention
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #49
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #50
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #51
SQL-Rank: A Listwise Approach to Collaborative Ranking
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #52
Extreme Learning to Rank via Low Rank Assumption
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #53
Adversarial Attack on Graph Structured Data
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #54
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #55
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #56
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #57
Thompson Sampling for Combinatorial Semi-Bandits
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #58
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #59
Deep Asymmetric Multi-task Feature Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #60
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #61
Stein Variational Message Passing for Continuous Graphical Models
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #62
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #63
Towards Binary-Valued Gates for Robust LSTM Training
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #64
Fitting New Speakers Based on a Short Untranscribed Sample
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #65
Stochastic Variance-Reduced Policy Gradient
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #66
Convergent Tree Backup and Retrace with Function Approximation
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #67
Alternating Randomized Block Coordinate Descent
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #68
Shampoo: Preconditioned Stochastic Tensor Optimization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #69
Stochastic Wasserstein Barycenters
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #70
Accelerating Natural Gradient with Higher-Order Invariance
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #71
Learning unknown ODE models with Gaussian processes
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #72
Constraining the Dynamics of Deep Probabilistic Models
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #73
Fast Decoding in Sequence Models Using Discrete Latent Variables
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #74
High Performance Zero-Memory Overhead Direct Convolutions
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #75
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #76
Improved large-scale graph learning through ridge spectral sparsification
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #77
Distilling the Posterior in Bayesian Neural Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #78
Scalable approximate Bayesian inference for particle tracking data
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #79
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #80
Practical Contextual Bandits with Regression Oracles
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #81
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #82
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #83
GAIN: Missing Data Imputation using Generative Adversarial Nets
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #84
Synthesizing Programs for Images using Reinforced Adversarial Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #85
Geometry Score: A Method For Comparing Generative Adversarial Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #86
Addressing Function Approximation Error in Actor-Critic Methods
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #87
Fast Bellman Updates for Robust MDPs
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #88
Configurable Markov Decision Processes
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #89
Prediction Rule Reshaping
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #90
Dimensionality-Driven Learning with Noisy Labels
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #91
Learning Memory Access Patterns
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #92
Geodesic Convolutional Shape Optimization
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #93
Visualizing and Understanding Atari Agents
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #94
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #95
Is Generator Conditioning Causally Related to GAN Performance?
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #96
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #97
Inductive Two-Layer Modeling with Parametric Bregman Transfer
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #98
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #99
Understanding Generalization and Optimization Performance of Deep CNNs
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #100
The Multilinear Structure of ReLU Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #101
Parallel and Streaming Algorithms for K-Core Decomposition
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #102
Fast Approximate Spectral Clustering for Dynamic Networks
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #103
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #104
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #105
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #106
Black-box Adversarial Attacks with Limited Queries and Information
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #107
Using Inherent Structures to design Lean 2-layer RBMs
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #108
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #109
Composable Planning with Attributes
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #110
Measuring abstract reasoning in neural networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #111
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #112
Self-Bounded Prediction Suffix Tree via Approximate String Matching
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #113
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #114
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #115
Composite Functional Gradient Learning of Generative Adversarial Models
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #116
LaVAN: Localized and Visible Adversarial Noise
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #117
Approximation Guarantees for Adaptive Sampling
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #118
Constrained Interacting Submodular Groupings
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #119
Residual Unfairness in Fair Machine Learning from Prejudiced Data
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #120
Adversarial Regression with Multiple Learners
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #121
Representation Tradeoffs for Hyperbolic Embeddings
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #122
Improving Sign Random Projections With Additional Information
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #123
Bandits with Delayed, Aggregated Anonymous Feedback
In
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Poster
Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #124
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #125
Learning Policy Representations in Multiagent Systems
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #126
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #127
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #128
Compressing Neural Networks using the Variational Information Bottelneck
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #129
Scalable Bilinear Pi Learning Using State and Action Features
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #130
Time Limits in Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #131
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #132
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #133
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #134
Bucket Renormalization for Approximate Inference
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #135
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #136
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #137
Tropical Geometry of Deep Neural Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #138
Learning Dynamics of Linear Denoising Autoencoders
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #139
Nonparametric variable importance using an augmented neural network with multi-task learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #140
Training Neural Machines with Trace-Based Supervision
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #141
Open Category Detection with PAC Guarantees
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #142
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #143
Learning Localized Spatio-Temporal Models From Streaming Data
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #144
Feasible Arm Identification
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #145
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #146
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #147
Towards Fast Computation of Certified Robustness for ReLU Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #148
A Two-Step Computation of the Exact GAN Wasserstein Distance
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #149
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #150
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #151
Accurate Uncertainties for Deep Learning Using Calibrated Regression
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #152
Neural Autoregressive Flows
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #153
Probabilistic Boolean Tensor Decomposition
In
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #154
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #155
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #156
Randomized Block Cubic Newton Method
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #157
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #158
Local Density Estimation in High Dimensions
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #159
To Understand Deep Learning We Need to Understand Kernel Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #160
Learning in Reproducing Kernel Kreı̆n Spaces
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #161
Functional Gradient Boosting based on Residual Network Perception
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #162
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #163
Characterizing Implicit Bias in Terms of Optimization Geometry
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #164
prDeep: Robust Phase Retrieval with a Flexible Deep Network
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #165
Adversarial Time-to-Event Modeling
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #166
MAGAN: Aligning Biological Manifolds
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #167
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #168
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #169
PixelSNAIL: An Improved Autoregressive Generative Model
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #170
Focused Hierarchical RNNs for Conditional Sequence Processing
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #171
Noise2Noise: Learning Image Restoration without Clean Data
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #172
Learning to Reweight Examples for Robust Deep Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #173
Policy and Value Transfer in Lifelong Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #174
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #175
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #176
Understanding the Loss Surface of Neural Networks for Binary Classification
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #177
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #178
Reviving and Improving Recurrent Back-Propagation
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #179
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #180
Learning Compact Neural Networks with Regularization
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #181
Investigating Human Priors for Playing Video Games
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #182
Decoupling Gradient-Like Learning Rules from Representations
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #183
Invariance of Weight Distributions in Rectified MLPs
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #184
Stronger Generalization Bounds for Deep Nets via a Compression Approach
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #185
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #186
Loss Decomposition for Fast Learning in Large Output Spaces
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #187
Stochastic Proximal Algorithms for AUC Maximization
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #188
Accelerated Spectral Ranking
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #189
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #190
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #191
Learning One Convolutional Layer with Overlapping Patches
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #192
A Spline Theory of Deep Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #193
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #194
Variational Bayesian dropout: pitfalls and fixes
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #195
Adversarial Learning with Local Coordinate Coding
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #196
Learning Representations and Generative Models for 3D Point Clouds
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #197
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #198
Noisy Natural Gradient as Variational Inference
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #199
Deep Variational Reinforcement Learning for POMDPs
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #200
Recurrent Predictive State Policy Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #201
The Mechanics of n-Player Differentiable Games
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #202
Improved Training of Generative Adversarial Networks Using Representative Features
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #203
Hierarchical Multi-Label Classification Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #204
Knowledge Transfer with Jacobian Matching
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #205
Towards Black-box Iterative Machine Teaching
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #206
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #207
Importance Weighted Transfer of Samples in Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #208
Beyond the One-Step Greedy Approach in Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #209
Optimization, fast and slow: optimally switching between local and Bayesian optimization
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #210
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #211
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #212
Approximate message passing for amplitude based optimization
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #214
Tempered Adversarial Networks
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #213
Delayed Impact of Fair Machine Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #215
Fast Information-theoretic Bayesian Optimisation
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #216
Tight Regret Bounds for Bayesian Optimization in One Dimension
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #218
Kernelized Synaptic Weight Matrices
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #219
A Distributed Second-Order Algorithm You Can Trust
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #220
On Acceleration with Noise-Corrupted Gradients
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #221
Gradient Coding from Cyclic MDS Codes and Expander Graphs
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #222
Accelerating Greedy Coordinate Descent Methods
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #223
Finding Influential Training Samples for Gradient Boosted Decision Trees
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #224
Improving Regression Performance with Distributional Losses
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #225
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #226
Learning to Act in Decentralized Partially Observable MDPs
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Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #227
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
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Break
Thu Jul 12 10:30 AM -- 11:00 AM (PDT) @ Hall B
Coffee Break
Break
Thu Jul 12 12:00 PM -- 01:30 PM (PDT)
Lunch - on your own
Break
Thu Jul 12 03:30 PM -- 04:00 PM (PDT) @ Hall B
Coffee Break
Break
Thu Jul 12 06:15 PM -- 07:15 PM (PDT) @ Hall B
Light Evening Snack
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ K23
31st International Workshop on Qualitative Reasoning (QR 2018)
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ K24
6th Goal Reasoning Workshop
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ T4
Computer Games Workshop
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ K12
FCA4AI 2018
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ B2
Joint Workshop on AI in Health (day 1)
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ B9
Linguistic and Cognitive Approaches To Dialog Agents (LaCATODA 2018)
Workshop
Thu Jul 12 11:30 PM -- 03:30 AM (PDT) @ K22
Tenth International Workshop Modelling and Reasoning in Context (MRC)
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ B3
The 3rd International workshop on biomedical informatics with optimization and machine learning (BOOM)
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ B5
The 3rd International Workshop on Knowledge Discovery in Healthcare Data
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ T3
Towards learning with limited labels: Equivariance, Invariance, and Beyond
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ K2
Fairness, Interpretability, and Explainability Federation of Workshops (day 1)
Workshop
Thu Jul 12 11:30 PM -- 09:00 AM (PDT) @ K16
Autonomy in Teams -- Joint Workshop on Sharing Autonomy in Human-Robot Interaction