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Break
Wed Jul 11 12:45 AM -- 01:15 AM (KST) @ Hall B
Coffee Break
Break
Wed Jul 11 03:30 AM -- 05:00 AM (KST)
Lunch - on your own
Break
Wed Jul 11 07:15 AM -- 07:45 AM (KST) @ Hall B
Coffee Break
Break
Wed Jul 11 10:00 AM -- 11:15 AM (KST) @ Hall B
Opening Reception
Invited Talk
Wed Jul 11 04:00 PM -- 05:00 PM (KST) @ A1
AI and Security: Lessons, Challenges and Future Directions
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Video]
Session
Wed Jul 11 05:00 PM -- 05:20 PM (KST) @ A1
Best Paper Session 1
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ K11
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
In
Clustering 1
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A3
Transfer Learning via Learning to Transfer
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ Victoria
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ K1 + K2
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A1
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A6
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A4
Crowdsourcing with Arbitrary Adversaries
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A9
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Oral
Wed Jul 11 06:00 PM -- 06:20 PM (KST) @ A7
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
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PDF]
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ A4
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
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PDF]
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ K1 + K2
Nonoverlap-Promoting Variable Selection
Oral
Wed Jul 11 06:20 PM -- 06:40 PM (KST) @ A3
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ Victoria
Learning to search with MCTSnets
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ A9
Distributed Nonparametric Regression under Communication Constraints
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PDF]
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ A1
Learning with Abandonment
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ A7
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
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PDF]
Oral
Wed Jul 11 06:20 PM -- 06:30 PM (KST) @ K11
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
In
Clustering 1
Oral
Wed Jul 11 06:20 PM -- 06:40 PM (KST) @ A5
SparseMAP: Differentiable Sparse Structured Inference
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ A6
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ A9
Coded Sparse Matrix Multiplication
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ A1
Lipschitz Continuity in Model-based Reinforcement Learning
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ A4
Conditional Noise-Contrastive Estimation of Unnormalised Models
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ K1 + K2
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ A7
Tree Edit Distance Learning via Adaptive Symbol Embeddings
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PDF]
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ K11
Hierarchical Clustering with Structural Constraints
In
Clustering 1
Oral
Wed Jul 11 06:30 PM -- 06:40 PM (KST) @ Victoria
Differentiable plasticity: training plastic neural networks with backpropagation
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ K11
K-means clustering using random matrix sparsification
In
Clustering 1
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A7
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
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PDF]
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ Victoria
TACO: Learning Task Decomposition via Temporal Alignment for Control
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A9
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A6
Variational Network Inference: Strong and Stable with Concrete Support
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ K1 + K2
Black Box FDR
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A1
Implicit Quantile Networks for Distributional Reinforcement Learning
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A5
Efficient and Consistent Adversarial Bipartite Matching
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A3
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Oral
Wed Jul 11 06:40 PM -- 06:50 PM (KST) @ A4
Deep One-Class Classification
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ K11
Clustering Semi-Random Mixtures of Gaussians
In
Clustering 1
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A1
More Robust Doubly Robust Off-policy Evaluation
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ K1 + K2
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ Victoria
Graph Networks as Learnable Physics Engines for Inference and Control
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A9
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A3
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
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PDF]
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A5
Learning to Speed Up Structured Output Prediction
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A6
Network Global Testing by Counting Graphlets
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A4
Deep Density Destructors
Oral
Wed Jul 11 06:50 PM -- 07:00 PM (KST) @ A7
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
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PDF]
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A4
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A9
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ K11
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A6
Data-Dependent Stability of Stochastic Gradient Descent
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A5
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ K1 + K2
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A1
Coordinated Exploration in Concurrent Reinforcement Learning
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A7
Improving Optimization in Models With Continuous Symmetry Breaking
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PDF]
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ A3
Stagewise Safe Bayesian Optimization with Gaussian Processes
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PDF]
Oral
Wed Jul 11 08:30 PM -- 08:50 PM (KST) @ Victoria
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A3
BOCK : Bayesian Optimization with Cylindrical Kernels
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PDF]
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ Victoria
Semi-Supervised Learning via Compact Latent Space Clustering
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A5
Nearly Optimal Robust Subspace Tracking
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ K1 + K2
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A6
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Oral
Wed Jul 11 08:50 PM -- 09:00 PM (KST) @ A1
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A4
Bayesian Quadrature for Multiple Related Integrals
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A9
signSGD: Compressed Optimisation for Non-Convex Problems
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ K11
Subspace Embedding and Linear Regression with Orlicz Norm
Oral
Wed Jul 11 08:50 PM -- 09:10 PM (KST) @ A7
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
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PDF]
Oral
Wed Jul 11 09:00 PM -- 09:10 PM (KST) @ A1
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A9
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A5
Safe Element Screening for Submodular Function Minimization
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ K11
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A4
Differentiable Compositional Kernel Learning for Gaussian Processes
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A3
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
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PDF]
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A1
Gated Path Planning Networks
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A6
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ A7
Learning Steady-States of Iterative Algorithms over Graphs
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PDF]
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ K1 + K2
An Estimation and Analysis Framework for the Rasch Model
Oral
Wed Jul 11 09:10 PM -- 09:20 PM (KST) @ Victoria
Conditional Neural Processes
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A4
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A1
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A5
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ Victoria
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A3
Bayesian Optimization of Combinatorial Structures
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PDF]
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ K11
Streaming Principal Component Analysis in Noisy Setting
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A6
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ K1 + K2
End-to-end Active Object Tracking via Reinforcement Learning
Oral
Wed Jul 11 09:20 PM -- 09:30 PM (KST) @ A9
$D^2$: Decentralized Training over Decentralized Data
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A5
The Limits of Maxing, Ranking, and Preference Learning
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A6
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
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PDF]
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A9
An Alternative View: When Does SGD Escape Local Minima?
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ K1 + K2
Deep Predictive Coding Network for Object Recognition
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ Victoria
Non-linear motor control by local learning in spiking neural networks
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ K11
Linear Spectral Estimators and an Application to Phase Retrieval
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A4
Variational Inference and Model Selection with Generalized Evidence Bounds
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A3
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
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PDF]
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A1
Structured Control Nets for Deep Reinforcement Learning
Oral
Wed Jul 11 09:30 PM -- 09:50 PM (KST) @ A7
Generative Temporal Models with Spatial Memory for Partially Observed Environments
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PDF]
Oral
Wed Jul 11 09:50 PM -- 10:00 PM (KST) @ A6
The Generalization Error of Dictionary Learning with Moreau Envelopes
Oral
Wed Jul 11 09:50 PM -- 10:00 PM (KST) @ K1 + K2
Gradually Updated Neural Networks for Large-Scale Image Recognition
Oral
Wed Jul 11 09:50 PM -- 10:10 PM (KST) @ A9
Escaping Saddles with Stochastic Gradients
Oral
Wed Jul 11 09:50 PM -- 10:00 PM (KST) @ A5
Learning a Mixture of Two Multinomial Logits
Oral
Wed Jul 11 09:50 PM -- 10:00 PM (KST) @ Victoria
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Oral
Wed Jul 11 09:50 PM -- 10:10 PM (KST) @ A3
Selecting Representative Examples for Program Synthesis
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Oral
Wed Jul 11 09:50 PM -- 10:10 PM (KST) @ A1
Latent Space Policies for Hierarchical Reinforcement Learning
Oral
Wed Jul 11 09:50 PM -- 10:10 PM (KST) @ K11
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Oral
Wed Jul 11 09:50 PM -- 10:10 PM (KST) @ A4
Fixing a Broken ELBO
Oral
Wed Jul 11 10:00 PM -- 10:10 PM (KST) @ K1 + K2
Neural Inverse Rendering for General Reflectance Photometric Stereo
Oral
Wed Jul 11 10:00 PM -- 10:10 PM (KST) @ A7
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
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PDF]
Oral
Wed Jul 11 10:00 PM -- 10:10 PM (KST) @ A6
On Learning Sparsely Used Dictionaries from Incomplete Samples
Oral
Wed Jul 11 10:00 PM -- 10:10 PM (KST) @ Victoria
Hierarchical Long-term Video Prediction without Supervision
Oral
Wed Jul 11 10:00 PM -- 10:10 PM (KST) @ A5
The Weighted Kendall and High-order Kernels for Permutations
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ K1 + K2
One-Shot Segmentation in Clutter
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ K11
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A9
Stochastic Variance-Reduced Cubic Regularized Newton Method
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ Victoria
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A3
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
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Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A6
The Well-Tempered Lasso
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A5
Parameterized Algorithms for the Matrix Completion Problem
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A1
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Oral
Wed Jul 11 10:10 PM -- 10:20 PM (KST) @ A4
Tighter Variational Bounds are Not Necessarily Better
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ A1
An Inference-Based Policy Gradient Method for Learning Options
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ A9
Non-convex Conditional Gradient Sliding
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ K11
Testing Sparsity over Known and Unknown Bases
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ Victoria
Model-Level Dual Learning
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ A6
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ K1 + K2
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ A7
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
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Oral
Wed Jul 11 10:20 PM -- 10:30 PM (KST) @ A4
Continuous-Time Flows for Efficient Inference and Density Estimation
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ Victoria
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A4
Semi-Implicit Variational Inference
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ K1 + K2
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A9
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A3
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
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Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ K11
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A5
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A7
Which Training Methods for GANs do actually Converge?
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Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A1
Programmatically Interpretable Reinforcement Learning
Oral
Wed Jul 11 11:00 PM -- 11:20 PM (KST) @ A6
Differentially Private Matrix Completion Revisited
Oral
Wed Jul 11 11:20 PM -- 11:30 PM (KST) @ Victoria
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Oral
Wed Jul 11 11:20 PM -- 11:30 PM (KST) @ A6
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ A4
Efficient Gradient-Free Variational Inference using Policy Search
Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ K1 + K2
The Dynamics of Learning: A Random Matrix Approach
Oral
Wed Jul 11 11:20 PM -- 11:30 PM (KST) @ A9
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ A3
Feedback-Based Tree Search for Reinforcement Learning
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Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ K11
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ A5
Representation Learning on Graphs with Jumping Knowledge Networks
Oral
Wed Jul 11 11:20 PM -- 11:40 PM (KST) @ A1
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Oral
Wed Jul 11 11:30 PM -- 11:40 PM (KST) @ Victoria
Kronecker Recurrent Units
Oral
Wed Jul 11 11:30 PM -- 11:40 PM (KST) @ A9
ADMM and Accelerated ADMM as Continuous Dynamical Systems
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Oral
Wed Jul 11 11:30 PM -- 11:40 PM (KST) @ A6
Local Private Hypothesis Testing: Chi-Square Tests
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A6
Locally Private Hypothesis Testing
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A5
Learning Diffusion using Hyperparameters
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A1
Automatic Goal Generation for Reinforcement Learning Agents
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A3
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
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Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ Victoria
Fast Parametric Learning with Activation Memorization
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ K1 + K2
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A9
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ K11
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A7
Learning Implicit Generative Models with the Method of Learned Moments
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Oral
Wed Jul 11 11:40 PM -- 11:50 PM (KST) @ A4
A Spectral Approach to Gradient Estimation for Implicit Distributions
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A3
Learning the Reward Function for a Misspecified Model
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Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ K11
Data Summarization at Scale: A Two-Stage Submodular Approach
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A4
Quasi-Monte Carlo Variational Inference
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A7
A Classification-Based Study of Covariate Shift in GAN Distributions
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Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A6
INSPECTRE: Privately Estimating the Unseen
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ K1 + K2
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ Victoria
Dynamic Evaluation of Neural Sequence Models
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A9
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A5
Canonical Tensor Decomposition for Knowledge Base Completion
Oral
Wed Jul 11 11:50 PM -- 12:00 AM (KST) @ A1
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
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