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June 9, 2019 Expo schedule » New in ML »

Timezone: America/Los_Angeles »

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Time	Room 201	Hall A	Grand Ballroom	Room 102	Seaside Ballroom	Room 101	Room 103	Room 104	Hall B
Tue Jun 11, 2019
10:00 AM (Orals)		Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
11:00 AM (Orals)	Applications Validating Causal Inference Models via Influence Functions	Deep Learning Algorithms SelectiveNet: A Deep Neural Network with an Integrated Reject Option	Adversarial Examples Adversarial Attacks on Node Embeddings via Graph Poisoning	Learning Theory: Games Regret Circuits: Composability of Regret Minimizers	Supervised Learning Data Shapley: Equitable Valuation of Data for Machine Learning	Approximate Inference A Contrastive Divergence for Combining Variational Inference and MCMC	Learning Theory Refined Complexity of PCA with Outliers	Non-convex Optimization PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization	Deep RL 1 ELF OpenGo: an analysis and open reimplementation of AlphaZero
11:20 AM (Orals)	Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks	Manifold Mixup: Better Representations by Interpolating Hidden States	First-Order Adversarial Vulnerability of Neural Networks and Input Dimension	Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function	Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data	Calibrated Approximate Bayesian Inference	On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms	Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization	Making Deep Q-learning methods robust to time discretization
11:25 AM (Orals)	Learning to Groove with Inverse Sequence Transformations	Processing Megapixel Images with Deep Attention-Sampling Models	On Certifying Non-Uniform Bounds against Adversarial Attacks	Stable-Predictive Optimistic Counterfactual Regret Minimization	Metric-Optimized Example Weights	Moment-Based Variational Inference for Markov Jump Processes	Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models	Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization	Nonlinear Distributional Gradient Temporal-Difference Learning
11:30 AM (Orals)	Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI	TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning	Improving Adversarial Robustness via Promoting Ensemble Diversity	When Samples Are Strategically Selected	Improving Model Selection by Employing the Test Data	Understanding MCMC Dynamics as Flows on the Wasserstein Space	Teaching a black-box learner	Lower Bounds for Smooth Nonconvex Finite-Sum Optimization	Composing Entropic Policies using Divergence Correction
11:35 AM (Orals)	HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving	Online Meta-Learning	Adversarial camera stickers: A physical camera-based attack on deep learning systems	Statistical Foundations of Virtual Democracy	Topological Data Analysis of Decision Boundaries with Application to Model Selection	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations	PAC Learnability of Node Functions in Networked Dynamical Systems	Nonconvex Variance Reduced Optimization with Arbitrary Sampling	TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
11:40 AM (Orals)	Molecular Hypergraph Grammar with Its Application to Molecular Optimization	Training Neural Networks with Local Error Signals	Adversarial examples from computational constraints	Optimal Auctions through Deep Learning	Contextual Memory Trees	Amortized Monte Carlo Integration	Online learning with kernel losses	Error Feedback Fixes SignSGD and other Gradient Compression Schemes	Multi-Agent Adversarial Inverse Reinforcement Learning
12:00 PM (Orals)	Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance	GMNN: Graph Markov Neural Networks	POPQORN: Quantifying Robustness of Recurrent Neural Networks	Learning to Clear the Market	Sparse Extreme Multi-label Learning with Oracle Property	Stein Point Markov Chain Monte Carlo	Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates	A Composite Randomized Incremental Gradient Method	Policy Consolidation for Continual Reinforcement Learning
12:05 PM (Orals)	Learning to Prove Theorems via Interacting with Proof Assistants	Self-Attention Graph Pooling	Using Pre-Training Can Improve Model Robustness and Uncertainty	Learning to bid in revenue-maximizing auctions	Shape Constraints for Set Functions	Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations	Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise	Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference	Off-Policy Deep Reinforcement Learning without Exploration
12:10 PM (Orals)	Circuit-GNN: Graph Neural Networks for Distributed Circuit Design	Combating Label Noise in Deep Learning using Abstention		Open-ended learning in symmetric zero-sum games	On The Power of Curriculum Learning in Training Deep Networks	Particle Flow Bayes' Rule	Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension	Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always	Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
12:15 PM (Orals)	Learning to Optimize Multigrid PDE Solvers	LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning	PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach	Deep Counterfactual Regret Minimization	Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration	Correlated Variational Auto-Encoders	Maximum Likelihood Estimation for Learning Populations of Parameters	Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number	Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
02:00 PM (Orals)	Time Series A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation	Generative Adversarial Networks Self-Attention Generative Adversarial Networks	Deep Learning Theory On Learning Invariant Representations for Domain Adaptation	Information Theory and Estimation Generalized Approximate Survey Propagation for High-Dimensional Estimation	Supervised Learning Robust Decision Trees Against Adversarial Examples	Kernel Methods Towards a Unified Analysis of Random Fourier Features	Convex Optimization Projection onto Minkowski Sums with Application to Constrained Learning	Reinforcement Learning Theory Safe Policy Improvement with Baseline Bootstrapping	Deep RL 2 An Investigation of Model-Free Planning
02:20 PM (Orals)	Learning Hawkes Processes Under Synchronization Noise	Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching	Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models	Boosted Density Estimation Remastered	Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth	Learning deep kernels for exponential family densities	Blended Conditonal Gradients	Distributional Reinforcement Learning for Efficient Exploration	CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
02:25 PM (Orals)	Generative Adversarial User Model for Reinforcement Learning Based Recommendation System	High-Fidelity Image Generation With Fewer Labels	Adversarial Generation of Time-Frequency Features with application in audio synthesis	Inference and Sampling of $K_{33}$-free Ising Models	Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation	Bayesian Deconditional Kernel Mean Embeddings	Acceleration of SVRG and Katyusha X by Inexact Preconditioning	Optimistic Policy Optimization via Multiple Importance Sampling	Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
02:30 PM (Orals)	A Statistical Investigation of Long Memory in Language and Music	Revisiting precision recall definition for generative modeling	On the Universality of Invariant Networks	Random Matrix Improved Covariance Estimation for a Large Class of Metrics	Optimal Transport for structured data with application on graphs	A Kernel Perspective for Regularizing Deep Neural Networks	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD	Neural Logic Reinforcement Learning	Diagnosing Bottlenecks in Deep Q-learning Algorithms
02:35 PM (Orals)	Deep Factors for Forecasting	Wasserstein of Wasserstein Loss for Learning Generative Models	Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks	Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication	Learning Optimal Linear Regularizers	A Persistent Weisfeiler--Lehman Procedure for Graph Classification	A Conditional-Gradient-Based Augmented Lagrangian Framework	Learning to Collaborate in Markov Decision Processes	Collaborative Evolutionary Reinforcement Learning
02:40 PM (Orals)	Weakly-Supervised Temporal Localization via Occurrence Count Learning	Flat Metric Minimization with Applications in Generative Modeling	Gauge Equivariant Convolutional Networks and the Icosahedral CNN	Neural Joint Source-Channel Coding	On Symmetric Losses for Learning from Corrupted Labels	Rehashing Kernel Evaluation in High Dimensions	SGD: General Analysis and Improved Rates	Predictor-Corrector Policy Optimization	EMI: Exploration with Mutual Information
03:00 PM (Orals)	Switching Linear Dynamics for Variational Bayes Filtering	Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs	Feature-Critic Networks for Heterogeneous Domain Generalization	Doubly-Competitive Distribution Estimation	AUCµ: A Performance Metric for Multi-Class Machine Learning Models	Large-Scale Sparse Kernel Canonical Correlation Analysis	Curvature-Exploiting Acceleration of Elastic Net Computations	Learning a Prior over Intent via Meta-Inverse Reinforcement Learning	Imitation Learning from Imperfect Demonstration
03:05 PM (Orals)	Imputing Missing Events in Continuous-Time Event Streams	Non-Parametric Priors For Generative Adversarial Networks	Learning to Convolve: A Generalized Weight-Tying Approach	Homomorphic Sensing	Regularization in directable environments with application to Tetris	A Kernel Theory of Modern Data Augmentation	Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication	DeepMDP: Learning Continuous Latent Space Models for Representation Learning	Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
03:10 PM (Orals)	Understanding and Controlling Memory in Recurrent Neural Networks	Lipschitz Generative Adversarial Nets	On Dropout and Nuclear Norm Regularization	Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements	Improved Dynamic Graph Learning through Fault-Tolerant Sparsification	kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection	Safe Grid Search with Optimal Complexity	Importance Sampling Policy Evaluation with an Estimated Behavior Policy	Dynamic Weights in Multi-Objective Deep Reinforcement Learning
03:15 PM (Orals)	Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces	HexaGAN: Generative Adversarial Nets for Real World Classification	Gradient Descent Finds Global Minima of Deep Neural Networks	Rate Distortion For Model Compression:From Theory To Practice	Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin	Scalable Learning in Reproducing Kernel Krein Spaces	SAGA with Arbitrary Sampling	Learning from a Learner	Fingerprint Policy Optimisation for Robust Reinforcement Learning
04:00 PM (Orals)	General ML Subspace Robust Wasserstein Distances	Deep Learning Architectures Graph Matching Networks for Learning the Similarity of Graph Structured Objects	Large Scale Learning and Systems Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm	Privacy Formal Privacy for Functional Data with Gaussian Perturbations	Applications: Computer Vision Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff	Bayesian Methods Dirichlet Simplex Nest and Geometric Inference	Statistical Learning Theory Natural Analysts in Adaptive Data Analysis	Reinforcement Learning Theory Separable value functions across time-scales	Deep Learning Optimization An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
04:20 PM (Orals)	Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models	BayesNAS: A Bayesian Approach for Neural Architecture Search	Sublinear Time Nearest Neighbor Search over Generalized Weighted Space	Graphical-model based estimation and inference for differential privacy	Collaborative Channel Pruning for Deep Networks	Bayesian leave-one-out cross-validation for large data	CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration	Learning Action Representations for Reinforcement Learning	Differentiable Linearized ADMM
04:25 PM (Orals)	Active Manifolds: A non-linear analogue to Active Subspaces	Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks	Compressing Gradient Optimizers via Count-Sketches	White-box vs Black-box: Bayes Optimal Strategies for Membership Inference	Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization	Rao-Blackwellized Stochastic Gradients for Discrete Distributions	Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction	Bayesian Counterfactual Risk Minimization	Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
04:30 PM (Orals)	Optimal Minimal Margin Maximization with Boosting	Shallow-Deep Networks: Understanding and Mitigating Network Overthinking	Scalable Fair Clustering	An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule	GDPP: Learning Diverse Generations using Determinantal Point Processes	Neurally-Guided Structure Inference	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	Per-Decision Option Discounting	A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
04:35 PM (Orals)	Generalized Linear Rule Models	Graph U-Nets	Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator	Sublinear Space Private Algorithms Under the Sliding Window Model	Co-Representation Network for Generalized Zero-Shot Learning	Bayesian Joint Spike-and-Slab Graphical Lasso	Optimality Implies Kernel Sum Classifiers are Statistically Efficient	Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds	The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
04:40 PM (Orals)	Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications	SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver	Fault Tolerance in Iterative-Convergent Machine Learning	Locally Private Bayesian Inference for Count Models	GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects	Rotation Invariant Householder Parameterization for Bayesian PCA	The Implicit Fairness Criterion of Unconstrained Learning	A Theory of Regularized Markov Decision Processes	AdaGrad stepsizes: sharp convergence over nonconvex landscapes
05:00 PM (Orals)	Variational Inference for sparse network reconstruction from count data	Area Attention	Static Automatic Batching In TensorFlow	Low Latency Privacy Preserving Inference	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	A Framework for Bayesian Optimization in Embedded Subspaces	Weak Detection of Signal in the Spiked Wigner Model	Discovering Options for Exploration by Minimizing Cover Time	Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
05:05 PM (Orals)	Simplifying Graph Convolutional Networks	The Evolved Transformer	Improving Neural Network Quantization without Retraining using Outlier Channel Splitting	Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters	Geometry Aware Convolutional Filters for Omnidirectional Images Representation	Convolutional Poisson Gamma Belief Network	Rademacher Complexity for Adversarially Robust Generalization	Policy Certificates: Towards Accountable Reinforcement Learning	SWALP : Stochastic Weight Averaging in Low Precision Training
05:10 PM (Orals)	Robust Influence Maximization for Hyperparametric Models	Jumpout : Improved Dropout for Deep Neural Networks with ReLUs	Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications	Poission Subsampled R\'enyi Differential Privacy	A Personalized Affective Memory Model for Improving Emotion Recognition	Automatic Posterior Transformation for Likelihood-Free Inference	Provably efficient RL with Rich Observations via Latent State Decoding	Action Robust Reinforcement Learning and Applications in Continuous Control	Efficient optimization of loops and limits with randomized telescoping sums
05:15 PM (Orals)	HyperGAN: A Generative Model for Diverse, Performant Neural Networks	Stochastic Deep Networks	DL2: Training and Querying Neural Networks with Logic	Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA	Temporal Gaussian Mixture Layer for Videos	Active Learning for Decision-Making from Imbalanced Observational Data	Information-Theoretic Considerations in Batch Reinforcement Learning	The Value Function Polytope in Reinforcement Learning	Self-similar Epochs: Value in arrangement

Time	Room 201	Hall A	Grand Ballroom	Room 102	Seaside Ballroom	Room 101	Room 103	Room 104	Hall B
Wed Jun 12, 2019
11:00 AM (Orals)	Applications Exploiting Worker Correlation for Label Aggregation in Crowdsourcing	Deep Generative Models Sum-of-Squares Polynomial Flow	Adversarial Examples Theoretically Principled Trade-off between Robustness and Accuracy	Robust Statistics and Machine Learning On the Convergence and Robustness of Adversarial Training	Reinforcement Learning and Bandits Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards	Gaussian Processes Distribution calibration for regression	Optimization Distributed Learning with Sublinear Communication	Deep Learning Theory Complexity of Linear Regions in Deep Networks	Deep RL Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
11:20 AM (Orals)	Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems	FloWaveNet : A Generative Flow for Raw Audio	The Odds are Odd: A Statistical Test for Detecting Adversarial Examples	Learning with Bad Training Data via Iterative Trimmed Loss Minimization	Target Tracking for Contextual Bandits: Application to Demand Side Management	Graph Convolutional Gaussian Processes	On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization	On Connected Sublevel Sets in Deep Learning	Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
11:25 AM (Orals)	A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology	Are Generative Classifiers More Robust to Adversarial Attacks?	ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation	On discriminative learning of prediction uncertainty	Correlated bandits or: How to minimize mean-squared error online	Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation	Stochastic Gradient Push for Distributed Deep Learning	Adversarial Examples Are a Natural Consequence of Test Error in Noise	Imitating Latent Policies from Observation
11:30 AM (Orals)	Fast and Flexible Inference of Joint Distributions from their Marginals	A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization	Certified Adversarial Robustness via Randomized Smoothing	Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels	Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging	GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver	Collective Model Fusion for Multiple Black-Box Experts	Greedy Layerwise Learning Can Scale To ImageNet	SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
11:35 AM (Orals)	Cognitive model priors for predicting human decisions	Disentangling Disentanglement in Variational Autoencoders	Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition	Does Data Augmentation Lead to Positive Margin?	Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models	Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization	On the Impact of the Activation function on Deep Neural Networks Training	Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
11:40 AM (Orals)	Conditioning by adaptive sampling for robust design	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE	Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization	Robust Learning from Untrusted Sources	Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously	AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs	Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning	Estimating Information Flow in Deep Neural Networks	Structured agents for physical construction
12:00 PM (Orals)	Direct Uncertainty Prediction for Medical Second Opinions	A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning	Wasserstein Adversarial Examples via Projected Sinkhorn Iterations	SELFIE: Refurbishing Unclean Samples for Robust Deep Learning	Bilinear Bandits with Low-rank Structure	End-to-End Probabilistic Inference for Nonstationary Audio Analysis	Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data	The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects	Learning Novel Policies For Tasks
12:05 PM (Orals)	Dynamic Measurement Scheduling for Event Forecasting using Deep RL	Emerging Convolutions for Generative Normalizing Flows	Transferable Clean-Label Poisoning Attacks on Deep Neural Nets	Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance	Online Learning to Rank with Features	Deep Gaussian Processes with Importance-Weighted Variational Inference	Noisy Dual Principal Component Pursuit	Characterizing Well-Behaved vs. Pathological Deep Neural Networks	Taming MAML: Efficient unbiased meta-reinforcement learning
12:10 PM (Orals)	Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization	A Large-Scale Study on Regularization and Normalization in GANs	NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks	Concentration Inequalities for Conditional Value at Risk	On the Design of Estimators for Bandit Off-Policy Evaluation	Automated Model Selection with Bayesian Quadrature	Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling	Understanding Geometry of Encoder-Decoder CNNs	Self-Supervised Exploration via Disagreement
12:15 PM (Orals)	DeepNose: Using artificial neural networks to represent the space of odorants	Variational Annealing of GANs: A Langevin Perspective	Simple Black-box Adversarial Attacks	Data Poisoning Attacks in Multi-Party Learning	Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem		Screening rules for Lasso with non-convex Sparse Regularizers	Traditional and Heavy Tailed Self Regularization in Neural Network Models	Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
02:00 PM (Orals)	Transfer and Multitask Learning Domain Agnostic Learning with Disentangled Representations	Deep Learning Architectures Invertible Residual Networks	Causality Causal Identification under Markov Equivalence: Completeness Results	Combinatorial Optimization Distributed Weighted Matching via Randomized Composable Coresets	Applications: Computer Vision Context-Aware Zero-Shot Learning for Object Recognition	Bayesian Non-parametrics Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior	Statistical Learning Theory Monge blunts Bayes: Hardness Results for Adversarial Training	Optimization: Convex and Non-convex Almost surely constrained convex optimization	Deep RL The Natural Language of Actions
02:20 PM (Orals)	Composing Value Functions in Reinforcement Learning	NAS-Bench-101: Towards Reproducible Neural Architecture Search	Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models	Multivariate Submodular Optimization	Band-limited Training and Inference for Convolutional Neural Networks	DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures	Better generalization with less data using robust gradient descent	Generalized Majorization-Minimization	Control Regularization for Reduced Variance Reinforcement Learning
02:25 PM (Orals)	Fast Context Adaptation via Meta-Learning	Approximated Oracle Filter Pruning for Destructive CNN Width Optimization	Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models	Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio	Learning Classifiers for Target Domain with Limited or No Labels	Random Function Priors for Correlation Modeling	Near optimal finite time identification of arbitrary linear dynamical systems	On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization	On the Generalization Gap in Reparameterizable Reinforcement Learning
02:30 PM (Orals)	Provable Guarantees for Gradient-Based Meta-Learning	LegoNet: Efficient Convolutional Neural Networks with Lego Filters	Classifying Treatment Responders Under Causal Effect Monotonicity	Approximating Orthogonal Matrices with Effective Givens Factorization	Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules	Variational Russian Roulette for Deep Bayesian Nonparametrics	Lossless or Quantized Boosting with Integer Arithmetic	Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization	Trajectory-Based Off-Policy Deep Reinforcement Learning
02:35 PM (Orals)	Towards Understanding Knowledge Distillation	Sorting Out Lipschitz Function Approximation	Learning Models from Data with Measurement Error: Tackling Underreporting	New results on information theoretic clustering	Anomaly Detection With Multiple-Hypotheses Predictions	Incorporating Grouping Information into Bayesian Decision Tree Ensembles	Orthogonal Random Forest for Causal Inference	Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization	A Deep Reinforcement Learning Perspective on Internet Congestion Control
02:40 PM (Orals)	Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers	Graph Element Networks: adaptive, structured computation and memory	Adjustment Criteria for Generalizing Experimental Findings	Improved Parallel Algorithms for Density-Based Network Clustering	Kernel Mean Matching for Content Addressability of GANs	Variational Implicit Processes	MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means	Efficient Dictionary Learning with Gradient Descent	Model-Based Active Exploration
03:00 PM (Orals)	Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation	Training CNNs with Selective Allocation of Channels	Conditional Independence in Testing Bayesian Networks	Submodular Observation Selection and Information Gathering for Quadratic Models	Neural Inverse Knitting: From Images to Manufacturing Instructions	Discovering Latent Covariance Structures for Multiple Time Series	The advantages of multiple classes for reducing overfitting from test set reuse	Plug-and-Play Methods Provably Converge with Properly Trained Denoisers	Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
03:05 PM (Orals)	Learning-to-Learn Stochastic Gradient Descent with Biased Regularization	Equivariant Transformer Networks	Sensitivity Analysis of Linear Structural Causal Models	Submodular Cost Submodular Cover with an Approximate Oracle	Making Convolutional Networks Shift-Invariant Again	Scalable Training of Inference Networks for Gaussian-Process Models	On the statistical rate of nonlinear recovery in generative models with heavy-tailed data	Riemannian adaptive stochastic gradient algorithms on matrix manifolds	Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
03:10 PM (Orals)	BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning	Overcoming Multi-model Forgetting	More Efficient Off-Policy Evaluation through Regularized Targeted Learning	Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity	Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation	Bayesian Optimization Meets Bayesian Optimal Stopping	Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!	Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence	A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
03:15 PM (Orals)	Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation	Bayesian Nonparametric Federated Learning of Neural Networks	Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding	Hiring Under Uncertainty	IMEXnet - A Forward Stable Deep Neural Network	Learning interpretable continuous-time models of latent stochastic dynamical systems	On Medians of (Randomized) Pairwise Means	Alternating Minimizations Converge to Second-Order Optimal Solutions	Remember and Forget for Experience Replay
04:00 PM (Orals)	Active Learning Active Embedding Search via Noisy Paired Comparisons	Deep Learning Algorithms How does Disagreement Help Generalization against Label Corruption?	Representation Learning Adversarially Learned Representations for Information Obfuscation and Inference	Networks and Relational Learning Position-aware Graph Neural Networks	Robust Statistics and Interpretability Do ImageNet Classifiers Generalize to ImageNet?	Probabilistic Inference A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes	Convex Optimization Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances	Reinforcement Learning Theory Provably Efficient Imitation Learning from Observation Alone	Generative Models Tensor Variable Elimination for Plated Factor Graphs
04:20 PM (Orals)	Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning	EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis	Adaptive Neural Trees	Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm	Exploring the Landscape of Spatial Robustness	Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models	SGD without Replacement: Sharper Rates for General Smooth Convex Functions	Dead-ends and Secure Exploration in Reinforcement Learning	Predicate Exchange: Inference with Declarative Knowledge
04:25 PM (Orals)	Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments	Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment	Connectivity-Optimized Representation Learning via Persistent Homology	Learning Generative Models across Incomparable Spaces	Sever: A Robust Meta-Algorithm for Stochastic Optimization	Understanding and Accelerating Particle-Based Variational Inference	On the Complexity of Approximating Wasserstein Barycenters	Statistics and Samples in Distributional Reinforcement Learning	Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
04:30 PM (Orals)	Bayesian Generative Active Deep Learning	Deep Compressed Sensing	Minimal Achievable Sufficient Statistic Learning	Relational Pooling for Graph Representations	Analyzing Federated Learning through an Adversarial Lens	Efficient learning of smooth probability functions from Bernoulli tests with guarantees	Estimate Sequences for Variance-Reduced Stochastic Composite Optimization	Hessian Aided Policy Gradient	Hierarchical Decompositional Mixtures of Variational Autoencoders
04:35 PM (Orals)	Active Learning for Probabilistic Structured Prediction of Cuts and Matchings	Differentiable Dynamic Normalization for Learning Deep Representation	Learning to Route in Similarity Graphs	Disentangled Graph Convolutional Networks	Fairwashing: the risk of rationalization	The Variational Predictive Natural Gradient	A Dynamical Systems Perspective on Nesterov Acceleration	Provably Efficient Maximum Entropy Exploration	Finding Mixed Nash Equilibria of Generative Adversarial Networks
04:40 PM (Orals)	Active Learning with Disagreement Graphs	Toward Understanding the Importance of Noise in Training Neural Networks	Invariant-Equivariant Representation Learning for Multi-Class Data	Open Vocabulary Learning on Source Code with a Graph-Structured Cache	Understanding the Origins of Bias in Word Embeddings	Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap	Random Shuffling Beats SGD after Finite Epochs	Combining parametric and nonparametric models for off-policy evaluation	CompILE: Compositional Imitation Learning and Execution
05:00 PM (Orals)	Multi-Frequency Vector Diffusion Maps	Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group	Infinite Mixture Prototypes for Few-shot Learning	Learning Discrete Structures for Graph Neural Networks	Bias Also Matters: Bias Attribution for Deep Neural Network Explanation	An Instability in Variational Inference for Topic Models	First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems	Sample-Optimal Parametric Q-Learning Using Linearly Additive Features	Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
05:05 PM (Orals)	Co-manifold learning with missing data	Breaking Inter-Layer Co-Adaptation by Classifier Anonymization	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing	Compositional Fairness Constraints for Graph Embeddings	Interpreting Adversarially Trained Convolutional Neural Networks	Bayesian Optimization of Composite Functions	Improved Convergence for $\ell_1$ and $\ell_\infty$ Regression via Iteratively Reweighted Least Squares	Transfer of Samples in Policy Search via Multiple Importance Sampling	Deep Generative Learning via Variational Gradient Flow
05:10 PM (Orals)		Understanding the Impact of Entropy on Policy Optimization	Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting	A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion	Counterfactual Visual Explanations	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions	Optimal Mini-Batch and Step Sizes for SAGA	Exploration Conscious Reinforcement Learning Revisited	Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
05:15 PM (Orals)		Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning		Stochastic Blockmodels meet Graph Neural Networks	Data Poisoning Attacks on Stochastic Bandits	Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization	Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory	Kernel-Based Reinforcement Learning in Robust Markov Decision Processes	Learning Neurosymbolic Generative Models via Program Synthesis

Time	Room 201	Hall A	Grand Ballroom	Room 102	Seaside Ballroom	Room 101	Room 103	Room 104	Hall B
Thu Jun 13, 2019
09:00 AM (Orals)	Applications Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random	Deep Generative Models State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations	Interpretability Neural Network Attributions: A Causal Perspective	Online Learning Matrix-Free Preconditioning in Online Learning	Privacy and Fairness On Sparse Linear Regression in the Local Differential Privacy Model	Monte Carlo Methods Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions	Supervised and Transfer Learning Geometric Losses for Distributional Learning	Non-convex Optimization Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning	Reinforcement Learning Batch Policy Learning under Constraints
09:20 AM (Orals)	Linear-Complexity Data-Parallel Earth Mover's Distance Approximations	Variational Laplace Autoencoders	Towards a Deep and Unified Understanding of Deep Neural Models in NLP	Online Convex Optimization in Adversarial Markov Decision Processes	Differentially Private Empirical Risk Minimization with Non-convex Loss Functions	Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization	Classification from Positive, Unlabeled and Biased Negative Data	Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization	Quantifying Generalization in Reinforcement Learning
09:25 AM (Orals)	Model Comparison for Semantic Grouping	Latent Normalizing Flows for Discrete Sequences	Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation	Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games	Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy	Unifying Orthogonal Monte Carlo Methods	Complementary-Label Learning for Arbitrary Losses and Models	Neuron birth-death dynamics accelerates gradient descent and converges asymptotically	Learning Latent Dynamics for Planning from Pixels
09:30 AM (Orals)	RaFM: Rank-Aware Factorization Machines	Multi-objective training of Generative Adversarial Networks with multiple discriminators	Functional Transparency for Structured Data: a Game-Theoretic Approach	Online Learning with Sleeping Experts and Feedback Graphs	Differentially Private Learning of Geometric Concepts	Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits	Learning to Infer Program Sketches	Width Provably Matters in Optimization for Deep Linear Neural Networks	Projections for Approximate Policy Iteration Algorithms
09:35 AM (Orals)	CAB: Continuous Adaptive Blending for Policy Evaluation and Learning	Learning Discrete and Continuous Factors of Data via Alternating Disentanglement	Exploring interpretable LSTM neural networks over multi-variable data	Incremental Randomized Sketching for Online Kernel Learning	Toward Controlling Discrimination in Online Ad Auctions	Metropolis-Hastings Generative Adversarial Networks	Hierarchically Structured Meta-learning	Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?	Learning Structured Decision Problems with Unawareness
09:40 AM (Orals)	MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement	Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables	TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing	Adaptive Scale-Invariant Online Algorithms for Learning Linear Models	Learning Optimal Fair Policies	Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets	Bridging Theory and Algorithm for Domain Adaptation	Power k-Means Clustering	Calibrated Model-Based Deep Reinforcement Learning
10:00 AM (Orals)	Neural Separation of Observed and Unobserved Distributions	Graphite: Iterative Generative Modeling of Graphs	Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute	Online Control with Adversarial Disturbances	Fairness-Aware Learning for Continuous Attributes and Treatments	Replica Conditional Sequential Monte Carlo	Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation	Distributed Learning over Unreliable Networks	Reinforcement Learning in Configurable Continuous Environments
10:05 AM (Orals)	Almost Unsupervised Text to Speech and Automatic Speech Recognition	Hybrid Models with Deep and Invertible Features	State-Regularized Recurrent Neural Networks	Adversarial Online Learning with noise	Fairness risk measures	A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes	Learning What and Where to Transfer	Escaping Saddle Points with Adaptive Gradient Methods	Target-Based Temporal-Difference Learning
10:10 AM (Orals)	AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss	MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets	Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation	Online Variance Reduction with Mixtures		Adaptive Antithetic Sampling for Variance Reduction		$\texttt{DoubleSqueeze}$: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression	Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
10:15 AM (Orals)	A fully differentiable beam search decoder	On Scalable and Efficient Computation of Large Scale Optimal Transport	On the Connection Between Adversarial Robustness and Saliency Map Interpretability	Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case		Accelerated Flow for Probability Distributions		Model Function Based Conditional Gradient Method with Armijo-like Line Search	Finding Options that Minimize Planning Time
11:00 AM (Orals)	Ranking and Preference Learning Scaling Up Ordinal Embedding: A Landmark Approach	Deep Learning Understanding and correcting pathologies in the training of learned optimizers	Deep Learning Theory Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem	Online Learning Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret	Fairness Proportionally Fair Clustering	Optimization and Graphical Models Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering	Unsupervised Learning DBSCAN++: Towards fast and scalable density clustering	Applications: Natural Language Processing Analogies Explained: Towards Understanding Word Embeddings	Deep Sequence Models Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
11:20 AM (Orals)	Learning to select for a predefined ranking	Demystifying Dropout	On the Spectral Bias of Neural Networks	Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems	Stable and Fair Classification	Dimensionality Reduction for Tukey Regression	Concrete Autoencoders: Differentiable Feature Selection and Reconstruction	Parameter-Efficient Transfer Learning for NLP	Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
11:25 AM (Orals)	Mallows ranking models: maximum likelihood estimate and regeneration	Ladder Capsule Network	Recursive Sketches for Modular Deep Learning	Adaptive Regret of Convex and Smooth Functions	Flexibly Fair Representation Learning by Disentanglement	Efficient Full-Matrix Adaptive Regularization	Gromov-Wasserstein Learning for Graph Matching and Node Embedding	Efficient On-Device Models using Neural Projections	Meta-Learning Neural Bloom Filters
11:30 AM (Orals)	Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models	Unreproducible Research is Reproducible	Zero-Shot Knowledge Distillation in Deep Networks	Online Adaptive Principal Component Analysis and Its extensions	Fair Regression: Quantitative Definitions and Reduction-Based Algorithms	Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms	Spectral Clustering of Signed Graphs via Matrix Power Means	Deep Residual Output Layers for Neural Language Generation	CoT: Cooperative Training for Generative Modeling of Discrete Data
11:35 AM (Orals)	Fast Algorithm for Generalized Multinomial Models with Ranking Data	Geometric Scattering for Graph Data Analysis	A Convergence Theory for Deep Learning via Over-Parameterization	POLITEX: Regret Bounds for Policy Iteration using Expert Prediction	Fairness without Harm: Decoupled Classifiers with Preference Guarantees	Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations	Coresets for Ordered Weighted Clustering	Improving Neural Language Modeling via Adversarial Training	Non-Monotonic Sequential Text Generation
11:40 AM (Orals)	Graph Resistance and Learning from Pairwise Comparisons	Robust Inference via Generative Classifiers for Handling Noisy Labels	A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks	Anytime Online-to-Batch, Optimism and Acceleration	Differentially Private Fair Learning	Robust Estimation of Tree Structured Gaussian Graphical Models	Fair k-Center Clustering for Data Summarization	Mixture Models for Diverse Machine Translation: Tricks of the Trade	Insertion Transformer: Flexible Sequence Generation via Insertion Operations
12:00 PM (Orals)	Learning Context-dependent Label Permutations for Multi-label Classification	LIT: Learned Intermediate Representation Training for Model Compression	Approximation and non-parametric estimation of ResNet-type convolutional neural networks	Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints	Obtaining Fairness using Optimal Transport Theory	Spectral Approximate Inference	A Better k-means++ Algorithm via Local Search	MASS: Masked Sequence to Sequence Pre-training for Language Generation	Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
12:05 PM (Orals)	Discovering Context Effects from Raw Choice Data	Analyzing and Improving Representations with the Soft Nearest Neighbor Loss	Global Convergence of Block Coordinate Descent in Deep Learning	Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning	Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions	Partially Linear Additive Gaussian Graphical Models	Kernel Normalized Cut: a Theoretical Revisit	Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops	Trainable Decoding of Sets of Sequences for Neural Sequence Models
12:10 PM (Orals)	On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference	What is the Effect of Importance Weighting in Deep Learning?	Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians	Adaptive Sensor Placement for Continuous Spaces	On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning	DAG-GNN: DAG Structure Learning with Graph Neural Networks	Guarantees for Spectral Clustering with Fairness Constraints	MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization	Learning to Generalize from Sparse and Underspecified Rewards
12:15 PM (Orals)	Learning Distance for Sequences by Learning a Ground Metric	Similarity of Neural Network Representations Revisited	On the Limitations of Representing Functions on Sets	Scale-free adaptive planning for deterministic dynamics & discounted rewards	Making Decisions that Reduce Discriminatory Impacts	Random Walks on Hypergraphs with Edge-Dependent Vertex Weights	Supervised Hierarchical Clustering with Exponential Linkage	CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network	Efficient Training of BERT by Progressively Stacking
03:00 PM (Orals)		Rates of Convergence for Sparse Variational Gaussian Process Regression
04:00 PM (Orals)		Representation Learning Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations	Bayesian Deep Learning Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering	Learning Theory Communication-Constrained Inference and the Role of Shared Randomness	Optimization Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications			Unsupervised Learning COMIC: Multi-view Clustering Without Parameter Selection	Bandits and Multiagent Learning Decentralized Exploration in Multi-Armed Bandits
04:20 PM (Orals)		Distributed, Egocentric Representations of Graphs for Detecting Critical Structures	Nonparametric Bayesian Deep Networks with Local Competition	Learning and Data Selection in Big Datasets	Online Algorithms for Rent-Or-Buy with Expert Advice			The Wasserstein Transform	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
04:25 PM (Orals)		Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities	Good Initializations of Variational Bayes for Deep Models	Sublinear quantum algorithms for training linear and kernel-based classifiers	Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity			Sequential Facility Location: Approximate Submodularity and Greedy Algorithm	Exploiting structure of uncertainty for efficient matroid semi-bandits
04:30 PM (Orals)		Multi-Object Representation Learning with Iterative Variational Inference	Dropout as a Structured Shrinkage Prior	Agnostic Federated Learning	Categorical Feature Compression via Submodular Optimization			Neural Collaborative Subspace Clustering	PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
04:35 PM (Orals)		Cross-Domain 3D Equivariant Image Embeddings	ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables	Discovering Conditionally Salient Features with Statistical Guarantees	Multi-Frequency Phase Synchronization			Unsupervised Deep Learning by Neighbourhood Discovery	Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
04:40 PM (Orals)		Loss Landscapes of Regularized Linear Autoencoders	On Variational Bounds of Mutual Information	A Theoretical Analysis of Contrastive Unsupervised Representation Learning	Faster Algorithms for Binary Matrix Factorization			Autoregressive Energy Machines	Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
05:00 PM (Orals)		Hyperbolic Disk Embeddings for Directed Acyclic Graphs	Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation	The information-theoretic value of unlabeled data in semi-supervised learning				Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization	TarMAC: Targeted Multi-Agent Communication
05:05 PM (Orals)		LatentGNN: Learning Efficient Non-local Relations for Visual Recognition	Hierarchical Importance Weighted Autoencoders	Unsupervised Label Noise Modeling and Loss Correction	Guided evolutionary strategies: augmenting random search with surrogate gradients			Noise2Self: Blind Denoising by Self-Supervision	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
05:10 PM (Orals)		Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness	Faster Attend-Infer-Repeat with Tractable Probabilistic Models	Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment	Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces			Learning Dependency Structures for Weak Supervision Models	Actor-Attention-Critic for Multi-Agent Reinforcement Learning
05:15 PM (Orals)		Lorentzian Distance Learning for Hyperbolic Representations	Understanding Priors in Bayesian Neural Networks at the Unit Level	Pareto Optimal Streaming Unsupervised Classification	Semi-Cyclic Stochastic Gradient Descent			Geometry and Symmetry in Short-and-Sparse Deconvolution	Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning

Tue Jun 11, 2019

Wed Jun 12, 2019

Thu Jun 13, 2019