ICML 2019 Events with Videos (original) (raw)

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• Opening Remarks Accepted Talks
• Two-level Explanations in Music Emotion Recognition
• NPR: Neural Personalised Ranking for Song Selection
• A Model-Driven Exploration of Accent Within the Amateur Singing Voice
• Interactive Neural Audio Synthesis
• Visualizing and Understanding Self-attention based Music Tagging
• A CycleGAN for style transfer between drum & bass subgenres Best Paper Talks
• Best Paper Talks Contributed Talks
• Contributed Talk 1: A Boosting Tree Based AutoML System for Lifelong Machine Learning
• Submodular Batch Selection for Training Deep Neural Networks
• Interpretability Contributed Talks
• Ngo Trong Trung
• Contributed Talk 2: Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents
• Exact Sampling of Determinantal Point Processes with Sublinear Time Preprocessing
• Contributed Talk 3: Random Search and Reproducibility for Neural Architecture Search
• Seq2Slate: Re-ranking and Slate Optimization with RNNs
• Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
• Contributed Talk: Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning
• Contributed Talk: Lifelong Learning via Online Leverage Score Sampling
• Contributed Talk: Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems
• Contributed Talk: Continual Adaptation for Efficient Machine Communication Contributed Talks
• Contributed Talks
• Contributed Talks
• Contributed Talks Contributed talks
• On Two Ways to use Determinantal Point Processes for Monte Carlo Integration
• Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
• Detecting Extrapolation with Influence Functions
• How Can We Be So Dense? The Robustness of Highly Sparse Representations
• Subspace Inference for Bayesian Deep Learning
• Quality of Uncertainty Quantification for Bayesian Neural Network Inference
• 'In-Between' Uncertainty in Bayesian Neural Networks
• Iris R. Seaman
• Using AI for Economic Upliftment of Handicraft Industry
• Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
• Faraz Torabi
• Seyed Kamyar Seyed Ghasemipour
• Nicholas R Waytowich
• Learning Global Variations in Outdoor PM_2.5 Concentrations with Satellite Images
• Pareto Efficient Fairness for Skewed Subgroup Data
• Crisis Sub-Events on Social Media: A Case Study of Wildfires
• Towards Detecting Dyslexia in Children's Handwriting Using Neural Networks
• Abhishek Das Crowdsourcing Deep Learning Phenomenas
• Crowdsourcing Deep Learning Phenomena Discussions
• Interpretability Invited Discussion: California's Senate Bill 10 (SB 10) on Pretrial Release and Detention with Solon Barocas and Peter Eckersley
• Panel discussion
• Human in the Loop Learning Panel Discussion Discussion Panels
• Panel Discussion moderated by Delip Rao Invited Talks
• Machine learning for robots to think fast
• Best Paper
• The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century
• Online Dictionary Learning for Sparse Coding
• Test of Time Award
• What 4 year olds can do and AI can’t (yet)
• Best Paper
• Keynote by Dan Roy: Progress on Nonvacuous Generalization Bounds
• Caroline Uhler
• Invited Talk 1: Adaptive Tolling for Multiagent Traffic Optimization
• Keynote by Chelsea Finn: Training for Generalization
• Invited Talk 2: The Strategic Perils of Learning from Historical Data
• Keynote by Sham Kakade: Prediction, Learning, and Memory
• Francisco LePort
• Invited Talk 3: Trend-Following Trading Strategies and Financial Market Stability
• Jeff Bilmes: Deep Submodular Synergies
• Keynote by Mikhail Belkin: A Hard Look at Generalization and its Theories
• Keynote by Jason Lee: On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization
• Michal Valko: How Negative Dependence Broke the Quadratic Barrier for Learning with Graphs and Kernels
• Invited Talk 5: Intra-day Stock Price Prediction as a Measure of Market Efficiency
• Sergei Levine: Distribution Matching and Mutual Information in Reinforcement Learning
• Suchi Saria (John Hopkins) - Link between Causal Inference and Reinforcement Learning and Applications to Learning from Offline/Observational Data
• Dawn Woodard (Uber) - Dynamic Pricing and Matching for Ride-Hailing
• Cheng Zhang: Active Mini-Batch Sampling using Repulsive Point Processes
• Building and Structuring Training Sets for Multi-Task Learning (Alex Ratner)
• Salman Avestimehr: Lagrange Coded Computing: Optimal Design for Resilient, Secure, and Private Distributed Learning
• Meta-Learning: Challenges and Frontiers (Chelsea Finn)
• Invited Talk by Professor Alexei Efros (UC Berkeley)
• Making Efficient use of Musical Annotations
• Emo Todorov
• Rashmi Vinayak: Resilient ML inference via coded computation: A learning-based approach
• Characterizing Musical Correlates of Large-Scale Discovery Behavior
• Pieter Abbeel
• Markus Weimer: A case for coded computing on elastic compute
• ARUBA: Efficient and Adaptive Meta-Learning with Provable Guarantees (Ameet Talwalkar)
• Personalization at Amazon Music
• Alex Dimakis: Coding Theory for Distributed Learning
• Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees (Massimiliano Pontil)
• Wei Zhang: Distributed deep learning system building at IBM: Scale-up and Scale-out case studies
• Multi-Task Learning in the Wilderness (Andrej Karpathy)
• CVPR19 Media Forensics workshop: a Preview
• Olga Russakovsky
• Recent Trends in Personalization: A Netflix Perspective (Justin Basilico)
• Invited Talk by Tom Van de Weghe (Stanford & VRT)
• User-curated shaping of expressive performances
• Toward Robust AI Systems for Understanding and Reasoning Over Multimodal Data (Hannaneh Hajishirzi) Invited Talk 1S
• Forcing Vision + Language Models To Actually See, Not Just Talk Invited Talk 2 (Bernt Schiele)s
• Topics in Vision and Language: Grounding, Segmentation and Author Anonymity Invited Talk 3S
• Learning to Reason: Modular and Relational Representations for Visual Questions and Referring Expressions Invited Talk 4S
• Multi-agent communication from raw perceptual input: what works, what doesn't and what's next Invited Talk 5S
• Overcoming Bias in Captioning Models Invited Talk 6 (Katerina Fragkiadaki)s
• Embodied language grounding Invited speakers
• AI Commons Invited talks
• Invited Talk: James Philbin
• Linearized two-layers neural networks in high dimension
• Hardware Efficiency Aware Neural Architecture Search and Compression
• Victor-Emmanuel Brunel: Negative Association and Discrete Determinantal Point Processes
• Invited Talk: Sanja Fidler
• Loss landscape and behaviour of algorithms in the spiked matrix-tensor model
• Structured matrices for efficient deep learning
• Why it's hard to mitigate climate change, and how to do better
• Tackling climate change challenges with AI through collaboration
• Understanding the Challenges of Algorithm and Hardware Co-design for Deep Neural Networks
• On the Interplay between Physics and Deep Learning
• Why Deep Learning Works: Traditional and Heavy-Tailed Implicit Self-Regularization in Deep Neural Networks
• Personalized Visualization of the Impact of Climate Change
• Advances in Climate Informatics: Machine Learning for the Study of Climate Change
• DNN Training and Inference with Hyper-Scaled Precision
• Is Optimization a sufficient language to understand Deep Learning?
• Mixed Precision Training & Inference
• Understanding overparameterized neural networks
• Geoscience data and models for the Climate Change AI community
• ML vs. Climate Change, Applications in Energy at DeepMind
• Sergey Levine: Unsupervised Reinforcement Learning and Meta-Learning
• Joyce Chai
• Vivienne Sze: Exploiting redundancy for efficient processing of DNNs and beyond
• Stefano Ermon
• Peter Stone: Learning Curricula for Transfer Learning in RL
• Jacob Andreas: Linguistic Scaffolds for Policy Learning
• Karol Hausman: Skill Representation and Supervision in Multi-Task Reinforcement Learning
• Martha White: Learning Representations for Continual Learning
• Natasha Jaques
• Pierre Sermanet
• Natalia Diaz-Rodriguez: Continual Learning and Robotics: an overview
• Andrew Saxe: Intriguing phenomena in training and generalization dynamics of deep networks
• Jeff Clune: Towards Solving Catastrophic Forgetting with Neuromodulation & Learning Curricula by Generating Environments
• Nicolas Heess: TBD
• Kris Kitani
• Benjamin Rosman: Exploiting Structure For Accelerating Reinforcement Learning Keynotes
• Keynote by Max Welling: A Nonparametric Bayesian Approach to Deep Learning (without GPs)
• Keynote by Kilian Weinberger: On Calibration and Fairness
• Keynote by Suchi Saria: Safety Challenges with Black-Box Predictors and Novel Learning Approaches for Failure Proofing
• Keynote by Dawn Song: Adversarial Machine Learning: Challenges, Lessons, and Future Directions
• Keynote by Terrance Boult: The Deep Unknown: on Open-set and Adversarial Examples in Deep Learning
• Doina Precup
• From Listening to Watching, A Recommender Systems Perspective
• Solving societal challenges with AI through partnerships
• AI for Ecology and Conservation
• AI for Whose Social Good?
• How to Volunteer as an AI Researcher
• Creating constructive change and avoiding unintended consequences from machine learning
• Assisting Vulnerable Communities through AI and OR: from Data to Deployed Decisions Keynote Talks
• Keynote by Peter Frazier: Grey-box Bayesian Optimization for AutoML
• Keynote by Rachel Thomas: Lessons Learned from Helping 200,000 non-ML experts use ML
• Keynote by Jeff Dean: An Overview of Google's Work on AutoML and Future Directions
• Keynote by Charles Sutton: Towards Semi-Automated Machine Learning Keynote talks
• Achieving Drawdown - Chad Frischmann Lightning Talks
• Tricks of Trade 2 (Rich Caruana) Opening Remarks
• Opening Remarks
• Opening Remarks
• Opening Remarks
• Opening Remarks Opening remarks
• Opening Remarks
• WCB Organisers Orals
• Adversarial Attacks on Node Embeddings via Graph Poisoning
• SelectiveNet: A Deep Neural Network with an Integrated Reject Option
• ELF OpenGo: an analysis and open reimplementation of AlphaZero
• A Contrastive Divergence for Combining Variational Inference and MCMC
• Refined Complexity of PCA with Outliers
• PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
• Validating Causal Inference Models via Influence Functions
• Data Shapley: Equitable Valuation of Data for Machine Learning
• First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
• Manifold Mixup: Better Representations by Interpolating Hidden States
• Making Deep Q-learning methods robust to time discretization
• 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
• Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
• Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
• On Certifying Non-Uniform Bounds against Adversarial Attacks
• Processing Megapixel Images with Deep Attention-Sampling Models
• Nonlinear Distributional Gradient Temporal-Difference Learning
• 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
• Learning to Groove with Inverse Sequence Transformations
• Metric-Optimized Example Weights
• Improving Adversarial Robustness via Promoting Ensemble Diversity
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
• Composing Entropic Policies using Divergence Correction
• Understanding MCMC Dynamics as Flows on the Wasserstein Space
• Teaching a black-box learner
• Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
• Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
• Improving Model Selection by Employing the Test Data
• Adversarial camera stickers: A physical camera-based attack on deep learning systems
• Online Meta-Learning
• TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
• 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
• HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
• Topological Data Analysis of Decision Boundaries with Application to Model Selection
• Adversarial examples from computational constraints
• Training Neural Networks with Local Error Signals
• Multi-Agent Adversarial Inverse Reinforcement Learning
• Amortized Monte Carlo Integration
• Online learning with kernel losses
• Error Feedback Fixes SignSGD and other Gradient Compression Schemes
• Molecular Hypergraph Grammar with Its Application to Molecular Optimization
• Contextual Memory Trees
• POPQORN: Quantifying Robustness of Recurrent Neural Networks
• GMNN: Graph Markov Neural Networks
• Policy Consolidation for Continual Reinforcement Learning
• 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
• Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
• Sparse Extreme Multi-label Learning with Oracle Property
• Using Pre-Training Can Improve Model Robustness and Uncertainty
• Self-Attention Graph Pooling
• Off-Policy Deep Reinforcement Learning without Exploration
• 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
• Learning to Prove Theorems via Interacting with Proof Assistants
• Shape Constraints for Set Functions
• Generalized No Free Lunch Theorem for Adversarial Robustness
• Combating Label Noise in Deep Learning using Abstention
• Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
• 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
• Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
• On The Power of Curriculum Learning in Training Deep Networks
• PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
• LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
• Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
• 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
• Learning to Optimize Multigrid PDE Solvers
• Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
• On Learning Invariant Representations for Domain Adaptation
• Self-Attention Generative Adversarial Networks
• An Investigation of Model-Free Planning
• Towards a Unified Analysis of Random Fourier Features
• Generalized Approximate Survey Propagation for High-Dimensional Estimation
• Projection onto Minkowski Sums with Application to Constrained Learning
• Safe Policy Improvement with Baseline Bootstrapping
• A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
• Robust Decision Trees Against Adversarial Examples
• Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
• Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
• Learning deep kernels for exponential family densities
• Boosted Density Estimation Remastered
• Blended Conditonal Gradients
• Distributional Reinforcement Learning for Efficient Exploration
• Learning Hawkes Processes Under Synchronization Noise
• Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
• Adversarial Generation of Time-Frequency Features with application in audio synthesis
• High-Fidelity Image Generation With Fewer Labels
• Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
• Bayesian Deconditional Kernel Mean Embeddings
• Inference and Sampling of K33K_{33}K33-free Ising Models
• Acceleration of SVRG and Katyusha X by Inexact Preconditioning
• Optimistic Policy Optimization via Multiple Importance Sampling
• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
• Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
• On the Universality of Invariant Networks
• Revisiting precision recall definition for generative modeling
• Diagnosing Bottlenecks in Deep Q-learning Algorithms
• A Kernel Perspective for Regularizing Deep Neural Networks
• Random Matrix Improved Covariance Estimation for a Large Class of Metrics
• Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
• Neural Logic Reinforcement Learning
• A Statistical Investigation of Long Memory in Language and Music
• Optimal Transport for structured data with application on graphs
• Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
• Wasserstein of Wasserstein Loss for Learning Generative Models
• Collaborative Evolutionary Reinforcement Learning
• A Persistent Weisfeiler--Lehman Procedure for Graph Classification
• Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
• A Conditional-Gradient-Based Augmented Lagrangian Framework
• Learning to Collaborate in Markov Decision Processes
• Deep Factors for Forecasting
• Learning Optimal Linear Regularizers
• Gauge Equivariant Convolutional Networks and the Icosahedral CNN
• Flat Metric Minimization with Applications in Generative Modeling
• EMI: Exploration with Mutual Information
• Rehashing Kernel Evaluation in High Dimensions
• Neural Joint Source-Channel Coding
• SGD: General Analysis and Improved Rates
• Predictor-Corrector Policy Optimization
• Weakly-Supervised Temporal Localization via Occurrence Count Learning
• On Symmetric Losses for Learning from Corrupted Labels
• Feature-Critic Networks for Heterogeneous Domain Generalization
• Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
• Imitation Learning from Imperfect Demonstration
• Large-Scale Sparse Kernel Canonical Correlation Analysis
• Doubly-Competitive Distribution Estimation
• Curvature-Exploiting Acceleration of Elastic Net Computations
• Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
• Switching Linear Dynamics for Variational Bayes Filtering
• Learning to Convolve: A Generalized Weight-Tying Approach
• Non-Parametric Priors For Generative Adversarial Networks
• Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
• A Kernel Theory of Modern Data Augmentation
• Homomorphic Sensing
• Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
• DeepMDP: Learning Continuous Latent Space Models for Representation Learning
• Imputing Missing Events in Continuous-Time Event Streams
• Regularization in directable environments with application to Tetris
• On Dropout and Nuclear Norm Regularization
• Lipschitz Generative Adversarial Nets
• Dynamic Weights in Multi-Objective Deep Reinforcement Learning
• kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
• Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
• Safe Grid Search with Optimal Complexity
• Importance Sampling Policy Evaluation with an Estimated Behavior Policy
• Understanding and Controlling Memory in Recurrent Neural Networks
• Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
• Gradient Descent Finds Global Minima of Deep Neural Networks
• HexaGAN: Generative Adversarial Nets for Real World Classification
• Fingerprint Policy Optimisation for Robust Reinforcement Learning
• Scalable Learning in Reproducing Kernel Krein Spaces
• Rate Distortion For Model Compression:From Theory To Practice
• SAGA with Arbitrary Sampling
• Learning from a Learner
• Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
• Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
• Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
• Dirichlet Simplex Nest and Geometric Inference
• Formal Privacy for Functional Data with Gaussian Perturbations
• Natural Analysts in Adaptive Data Analysis
• Separable value functions across time-scales
• Subspace Robust Wasserstein Distances
• Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
• Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
• BayesNAS: A Bayesian Approach for Neural Architecture Search
• Differentiable Linearized ADMM
• Bayesian leave-one-out cross-validation for large data
• Graphical-model based estimation and inference for differential privacy
• CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
• Learning Action Representations for Reinforcement Learning
• Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
• Collaborative Channel Pruning for Deep Networks
• Compressing Gradient Optimizers via Count-Sketches
• Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
• Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
• Rao-Blackwellized Stochastic Gradients for Discrete Distributions
• White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
• Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
• Bayesian Counterfactual Risk Minimization
• Active Manifolds: A non-linear analogue to Active Subspaces
• Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
• Scalable Fair Clustering
• Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
• A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
• Neurally-Guided Structure Inference
• Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
• Per-Decision Option Discounting
• Optimal Minimal Margin Maximization with Boosting
• GDPP: Learning Diverse Generations using Determinantal Point Processes
• Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
• Graph U-Nets
• The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
• Bayesian Joint Spike-and-Slab Graphical Lasso
• Sublinear Space Private Algorithms Under the Sliding Window Model
• Optimality Implies Kernel Sum Classifiers are Statistically Efficient
• Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
• Generalized Linear Rule Models
• Fault Tolerance in Iterative-Convergent Machine Learning
• SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
• AdaGrad stepsizes: sharp convergence over nonconvex landscapes
• Rotation Invariant Householder Parameterization for Bayesian PCA
• Locally Private Bayesian Inference for Count Models
• The Implicit Fairness Criterion of Unconstrained Learning
• A Theory of Regularized Markov Decision Processes
• Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
• GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
• Static Automatic Batching In TensorFlow
• Area Attention
• Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
• A Framework for Bayesian Optimization in Embedded Subspaces
• Low Latency Privacy Preserving Inference
• Weak Detection of Signal in the Spiked Wigner Model
• Discovering Options for Exploration by Minimizing Cover Time
• Variational Inference for sparse network reconstruction from count data
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
• The Evolved Transformer
• SWALP : Stochastic Weight Averaging in Low Precision Training
• Convolutional Poisson Gamma Belief Network
• Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
• Rademacher Complexity for Adversarially Robust Generalization
• Policy Certificates: Towards Accountable Reinforcement Learning
• Simplifying Graph Convolutional Networks
• Geometry Aware Convolutional Filters for Omnidirectional Images Representation
• Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
• Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
• Efficient optimization of loops and limits with randomized telescoping sums
• Automatic Posterior Transformation for Likelihood-Free Inference
• Poission Subsampled R\'enyi Differential Privacy
• Provably efficient RL with Rich Observations via Latent State Decoding
• Action Robust Reinforcement Learning and Applications in Continuous Control
• Robust Influence Maximization for Hyperparametric Models
• A Personalized Affective Memory Model for Improving Emotion Recognition
• DL2: Training and Querying Neural Networks with Logic
• Stochastic Deep Networks
• Self-similar Epochs: Value in arrangement
• Active Learning for Decision-Making from Imbalanced Observational Data
• Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
• Information-Theoretic Considerations in Batch Reinforcement Learning
• The Value Function Polytope in Reinforcement Learning
• HyperGAN: A Generative Model for Diverse, Performant Neural Networks
• Temporal Gaussian Mixture Layer for Videos
• Theoretically Principled Trade-off between Robustness and Accuracy
• Sum-of-Squares Polynomial Flow
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
• Distribution calibration for regression
• On the Convergence and Robustness of Adversarial Training
• Distributed Learning with Sublinear Communication
• Complexity of Linear Regions in Deep Networks
• Exploiting Worker Correlation for Label Aggregation in Crowdsourcing
• Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
• The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
• FloWaveNet : A Generative Flow for Raw Audio
• Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
• Graph Convolutional Gaussian Processes
• Learning with Bad Training Data via Iterative Trimmed Loss Minimization
• On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
• On Connected Sublevel Sets in Deep Learning
• Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
• Target Tracking for Contextual Bandits: Application to Demand Side Management
• ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
• Are Generative Classifiers More Robust to Adversarial Attacks?
• Imitating Latent Policies from Observation
• Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
• Adversarial Examples Are a Natural Consequence of Test Error in Noise
• A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
• Correlated bandits or: How to minimize mean-squared error online
• Certified Adversarial Robustness via Randomized Smoothing
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
• GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
• Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
• Collective Model Fusion for Multiple Black-Box Experts
• Greedy Layerwise Learning Can Scale To ImageNet
• Fast and Flexible Inference of Joint Distributions from their Marginals
• Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
• Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
• Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
• Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
• Does Data Augmentation Lead to Positive Margin?
• Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
• On the Impact of the Activation function on Deep Neural Networks Training
• Cognitive model priors for predicting human decisions
• Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
• Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
• Structured agents for physical construction
• AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
• Robust Learning from Untrusted Sources
• Trimming the ell_1\ell_1ell_1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning
• Estimating Information Flow in Deep Neural Networks
• Conditioning by adaptive sampling for robust design
• Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
• Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
• Learning Novel Policies For Tasks
• End-to-End Probabilistic Inference for Nonstationary Audio Analysis
• SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
• 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
• Direct Uncertainty Prediction for Medical Second Opinions
• Bilinear Bandits with Low-rank Structure
• Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
• Taming MAML: Efficient unbiased meta-reinforcement learning
• Deep Gaussian Processes with Importance-Weighted Variational Inference
• Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
• Noisy Dual Principal Component Pursuit
• Characterizing Well-Behaved vs. Pathological Deep Neural Networks
• Dynamic Measurement Scheduling for Event Forecasting using Deep RL
• NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
• Self-Supervised Exploration via Disagreement
• Automated Model Selection with Bayesian Quadrature
• Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
• Understanding Geometry of Encoder-Decoder CNNs
• Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
• On the Design of Estimators for Bandit Off-Policy Evaluation
• Simple Black-box Adversarial Attacks
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
• Data Poisoning Attacks in Multi-Party Learning
• Screening rules for Lasso with non-convex Sparse Regularizers
• Traditional and Heavy Tailed Self Regularization in Neural Network Models
• DeepNose: Using artificial neural networks to represent the space of odorants
• Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
• Causal Identification under Markov Equivalence: Completeness Results
• Invertible Residual Networks
• The Natural Language of Actions
• Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior
• Monge blunts Bayes: Hardness Results for Adversarial Training
• Almost surely constrained convex optimization
• Domain Agnostic Learning with Disentangled Representations
• Context-Aware Zero-Shot Learning for Object Recognition
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• Control Regularization for Reduced Variance Reinforcement Learning
• 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
• Composing Value Functions in Reinforcement Learning
• Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
• Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
• On the Generalization Gap in Reparameterizable Reinforcement Learning
• 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
• Fast Context Adaptation via Meta-Learning
• Classifying Treatment Responders Under Causal Effect Monotonicity
• LegoNet: Efficient Convolutional Neural Networks with Lego Filters
• Trajectory-Based Off-Policy Deep Reinforcement Learning
• 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
• Provable Guarantees for Gradient-Based Meta-Learning
• Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
• Learning Models from Data with Measurement Error: Tackling Underreporting
• Sorting Out Lipschitz Function Approximation
• A Deep Reinforcement Learning Perspective on Internet Congestion Control
• 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
• Towards Understanding Knowledge Distillation
• Anomaly Detection With Multiple-Hypotheses Predictions
• Adjustment Criteria for Generalizing Experimental Findings
• Graph Element Networks: adaptive, structured computation and memory
• Model-Based Active Exploration
• Variational Implicit Processes
• MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
• Efficient Dictionary Learning with Gradient Descent
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
• Kernel Mean Matching for Content Addressability of GANs
• Conditional Independence in Testing Bayesian Networks
• Training CNNs with Selective Allocation of Channels
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
• 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
• Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
• Neural Inverse Knitting: From Images to Manufacturing Instructions
• Sensitivity Analysis of Linear Structural Causal Models
• Equivariant Transformer Networks
• Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
• 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
• Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
• Making Convolutional Networks Shift-Invariant Again
• More Efficient Off-Policy Evaluation through Regularized Targeted Learning
• Overcoming Multi-model Forgetting
• A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
• 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
• BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
• Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
• Bayesian Nonparametric Federated Learning of Neural Networks
• Remember and Forget for Experience Replay
• Learning interpretable continuous-time models of latent stochastic dynamical systems
• On Medians of (Randomized) Pairwise Means
• Alternating Minimizations Converge to Second-Order Optimal Solutions
• Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
• IMEXnet - A Forward Stable Deep Neural Network
• Adversarially Learned Representations for Information Obfuscation and Inference
• How does Disagreement Help Generalization against Label Corruption?
• Tensor Variable Elimination for Plated Factor Graphs
• A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
• Position-aware Graph Neural Networks
• Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
• Provably Efficient Imitation Learning from Observation Alone
• Active Embedding Search via Noisy Paired Comparisons
• Do ImageNet Classifiers Generalize to ImageNet?
• Adaptive Neural Trees
• EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
• Predicate Exchange: Inference with Declarative Knowledge
• Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
• Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm
• SGD without Replacement: Sharper Rates for General Smooth Convex Functions
• Dead-ends and Secure Exploration in Reinforcement Learning
• Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning
• Exploring the Landscape of Spatial Robustness
• Connectivity-Optimized Representation Learning via Persistent Homology
• Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
• Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
• Understanding and Accelerating Particle-Based Variational Inference
• Learning Generative Models across Incomparable Spaces
• On the Complexity of Approximating Wasserstein Barycenters
• Statistics and Samples in Distributional Reinforcement Learning
• Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
• Sever: A Robust Meta-Algorithm for Stochastic Optimization
• Minimal Achievable Sufficient Statistic Learning
• Deep Compressed Sensing
• Hierarchical Decompositional Mixtures of Variational Autoencoders
• Efficient learning of smooth probability functions from Bernoulli tests with guarantees
• Relational Pooling for Graph Representations
• Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
• Hessian Aided Policy Gradient
• Bayesian Generative Active Deep Learning
• Analyzing Federated Learning through an Adversarial Lens
• Learning to Route in Similarity Graphs
• Differentiable Dynamic Normalization for Learning Deep Representation
• Finding Mixed Nash Equilibria of Generative Adversarial Networks
• The Variational Predictive Natural Gradient
• Disentangled Graph Convolutional Networks
• A Dynamical Systems Perspective on Nesterov Acceleration
• Provably Efficient Maximum Entropy Exploration
• Active Learning for Probabilistic Structured Prediction of Cuts and Matchings
• Fairwashing: the risk of rationalization
• Invariant-Equivariant Representation Learning for Multi-Class Data
• Toward Understanding the Importance of Noise in Training Neural Networks
• CompILE: Compositional Imitation Learning and Execution
• Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
• Open Vocabulary Learning on Source Code with a Graph-Structured Cache
• Random Shuffling Beats SGD after Finite Epochs
• Combining parametric and nonparametric models for off-policy evaluation
• Active Learning with Disagreement Graphs
• Understanding the Origins of Bias in Word Embeddings
• Infinite Mixture Prototypes for Few-shot Learning
• Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
• Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
• An Instability in Variational Inference for Topic Models
• Learning Discrete Structures for Graph Neural Networks
• First-Order Algorithms Converge Faster than O(1/k)O(1/k)O(1/k) on Convex Problems
• Sample-Optimal Parametric Q-Learning Using Linearly Additive Features
• Multi-Frequency Vector Diffusion Maps
• Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
• MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
• Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
• Deep Generative Learning via Variational Gradient Flow
• Bayesian Optimization of Composite Functions
• Compositional Fairness Constraints for Graph Embeddings
• Improved Convergence for ell1\ell_1ell1 and ellinfty\ell_\inftyellinfty Regression via Iteratively Reweighted Least Squares
• Transfer of Samples in Policy Search via Multiple Importance Sampling
• Co-manifold learning with missing data
• Interpreting Adversarially Trained Convolutional Neural Networks
• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
• Understanding the Impact of Entropy on Policy Optimization
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
• A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion
• Optimal Mini-Batch and Step Sizes for SAGA
• Exploration Conscious Reinforcement Learning Revisited
• Counterfactual Visual Explanations
• Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
• Learning Neurosymbolic Generative Models via Program Synthesis
• Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization
• Stochastic Blockmodels meet Graph Neural Networks
• Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
• Kernel-Based Reinforcement Learning in Robust Markov Decision Processes
• Data Poisoning Attacks on Stochastic Bandits
• Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
• Matrix-Free Preconditioning in Online Learning
• Geometric Losses for Distributional Learning
• Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
• Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
• On Sparse Linear Regression in the Local Differential Privacy Model
• Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
• Online Convex Optimization in Adversarial Markov Decision Processes
• Classification from Positive, Unlabeled and Biased Negative Data
• Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
• Linear-Complexity Data-Parallel Earth Mover's Distance Approximations
• Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
• Unifying Orthogonal Monte Carlo Methods
• Complementary-Label Learning for Arbitrary Losses and Models
• Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
• Model Comparison for Semantic Grouping
• Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
• Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
• Online Learning with Sleeping Experts and Feedback Graphs
• Learning to Infer Program Sketches
• Width Provably Matters in Optimization for Deep Linear Neural Networks
• RaFM: Rank-Aware Factorization Machines
• Differentially Private Learning of Geometric Concepts
• Metropolis-Hastings Generative Adversarial Networks
• Hierarchically Structured Meta-learning
• Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
• CAB: Continuous Adaptive Blending for Policy Evaluation and Learning
• Toward Controlling Discrimination in Online Ad Auctions
• Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
• Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
• Bridging Theory and Algorithm for Domain Adaptation
• Power k-Means Clustering
• MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
• Learning Optimal Fair Policies
• Replica Conditional Sequential Monte Carlo
• Online Control with Adversarial Disturbances
• Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
• Distributed Learning over Unreliable Networks
• Neural Separation of Observed and Unobserved Distributions
• Fairness-Aware Learning for Continuous Attributes and Treatments
• A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
• Adversarial Online Learning with noise
• Learning What and Where to Transfer
• Escaping Saddle Points with Adaptive Gradient Methods
• Almost Unsupervised Text to Speech and Automatic Speech Recognition
• Fairness risk measures
• Adaptive Antithetic Sampling for Variance Reduction
• Online Variance Reduction with Mixtures
• $\texttt{DoubleSqueeze}$: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression
• AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
• Accelerated Flow for Probability Distributions
• Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
• Model Function Based Conditional Gradient Method with Armijo-like Line Search
• A fully differentiable beam search decoder
• Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
• Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel kkk-means Clustering
• Learning Linear-Quadratic Regulators Efficiently with only sqrtT\sqrt{T}sqrtT Regret
• DBSCAN++: Towards fast and scalable density clustering
• Analogies Explained: Towards Understanding Word Embeddings
• Scaling Up Ordinal Embedding: A Landmark Approach
• Proportionally Fair Clustering
• On the Spectral Bias of Neural Networks
• Dimensionality Reduction for Tukey Regression
• Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
• Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
• Parameter-Efficient Transfer Learning for NLP
• Learning to select for a predefined ranking
• Stable and Fair Classification
• Recursive Sketches for Modular Deep Learning
• Efficient Full-Matrix Adaptive Regularization
• Adaptive Regret of Convex and Smooth Functions
• Gromov-Wasserstein Learning for Graph Matching and Node Embedding
• Efficient On-Device Models using Neural Projections
• Mallows ranking models: maximum likelihood estimate and regeneration
• Flexibly Fair Representation Learning by Disentanglement
• Zero-Shot Knowledge Distillation in Deep Networks
• Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
• Online Adaptive Principal Component Analysis and Its extensions
• Spectral Clustering of Signed Graphs via Matrix Power Means
• Deep Residual Output Layers for Neural Language Generation
• Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
• Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
• A Convergence Theory for Deep Learning via Over-Parameterization
• Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
• POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
• Improving Neural Language Modeling via Adversarial Training
• Fast Algorithm for Generalized Multinomial Models with Ranking Data
• Fairness without Harm: Decoupled Classifiers with Preference Guarantees
• A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
• Robust Estimation of Tree Structured Gaussian Graphical Models
• Anytime Online-to-Batch, Optimism and Acceleration
• Fair k-Center Clustering for Data Summarization
• Mixture Models for Diverse Machine Translation: Tricks of the Trade
• Graph Resistance and Learning from Pairwise Comparisons
• Differentially Private Fair Learning
• Approximation and non-parametric estimation of ResNet-type convolutional neural networks
• Spectral Approximate Inference
• Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
• A Better k-means++ Algorithm via Local Search
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
• Learning Context-dependent Label Permutations for Multi-label Classification
• Obtaining Fairness using Optimal Transport Theory
• Global Convergence of Block Coordinate Descent in Deep Learning
• Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
• Kernel Normalized Cut: a Theoretical Revisit
• Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
• Discovering Context Effects from Raw Choice Data
• Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
• Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
• DAG-GNN: DAG Structure Learning with Graph Neural Networks
• Adaptive Sensor Placement for Continuous Spaces
• Guarantees for Spectral Clustering with Fairness Constraints
• MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
• On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
• On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
• On the Limitations of Representing Functions on Sets
• Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
• Scale-free adaptive planning for deterministic dynamics & discounted rewards
• Supervised Hierarchical Clustering with Exponential Linkage
• CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
• Learning Distance for Sequences by Learning a Ground Metric
• COMIC: Multi-view Clustering Without Parameter Selection
• Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
• The Wasserstein Transform
• Online Algorithms for Rent-Or-Buy with Expert Advice
• Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
• Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
• Neural Collaborative Subspace Clustering
• Categorical Feature Compression via Submodular Optimization
• Unsupervised Deep Learning by Neighbourhood Discovery
• Multi-Frequency Phase Synchronization
• Autoregressive Energy Machines
• Faster Algorithms for Binary Matrix Factorization
• Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization
• Noise2Self: Blind Denoising by Self-Supervision
• Guided evolutionary strategies: augmenting random search with surrogate gradients
• Learning Dependency Structures for Weak Supervision Models
• Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
• Geometry and Symmetry in Short-and-Sparse Deconvolution
• Semi-Cyclic Stochastic Gradient Descent
• Analyzing the dynamics of online learning in over-parameterized two-layer neural networks
• Convergence Properties of Neural Networks on Separable Data
• Towards Understanding Regularization in Batch Normalization
• How Noise during Training Affects the Hessian Spectrum
• Asymptotics of Wide Networks from Feynman Diagrams
• A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
• Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region
• Learning the Arrow of Time Oral Presenations
• Contributed Talk: We Need No Pixels: Video Manipulation Detection Using Stream Descriptors Oral Presentations
• Contributed Talk: Limits of Deepfake Detection: A Robust Estimation Viewpoint
• Contributed Talk: A Utility-Preserving GAN for Face Obscuration
• Contributed Talk: Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems Oral presentations
• Learning Latent Plans from Play
• Revisiting Self-Supervised Visual Representation Learning
• Data-Efficient Image Recognition with Contrastive Predictive Coding Organizer's introductions
• Opening Remarks Panels
• Panel Discussion (moderator: Tom Dietterich)
• Panel Discussion
• Discussion Panel
• Panel Session Panel Discussions
• Panel Discussion
• Panel Discussion: Kevin Murphy, Nati Srebro, Aude Oliva, Andrew Saxe, Olga Russakovsky Moderator: Ali Rahimi
• Panel Discussion
• Best Paper Award and Panel Discussion Posters
• Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations Presentations
• The How2 Database and Challenge Problem proposals
• Detecting Waterborne Debris with Sim2Real and Randomization
• Conversational agents to address abusive online behaviors
• AI for Mitigating Effects of Climate and Weather Changes in Agriculture Spotlights
• Spotlight
• A Meta-Analysis of Overfitting in Machine Learning
• Uniform convergence may be unable to explain generalization in deep learning
• Towards Task and Architecture-Independent Generalization Gap Predictors
• Data-Dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
• Towards Large Scale Structure of the Loss Landscape of Neural Networks
• Zero-Shot Learning from scratch: leveraging local compositional representations
• Overparameterization without Overfitting: Jacobian-based Generalization Guarantees for Neural Networks
• How Learning Rate and Delay Affect Minima Selection in AsynchronousTraining of Neural Networks: Toward Closing the Generalization Gap
• Bad Global Minima Exist and SGD Can Reach Them
• Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
• Do deep neural networks learn shallow learnable examples first?
• On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width Spotlight Presentations
• "Locality Driven Coded Computation" Michael Rudow, Rashmi Vinayak and Venkat Guruswami
• "CodeNet: Training Large-Scale Neural Networks in Presence of Soft-Errors," Sanghamitra Dutta, Ziqian Bai, Tze Meng Low and Pulkit Grover
• "Reliable Clustering with Redundant Data Assignment" Venkat Gandikota, Arya Mazumdar and Ankit Singh Rawat
• "OverSketched Newton: Fast Convex Optimization for Serverless Systems," Vipul Gupta, Swanand Kadhe, Thomas Courtade, Michael Mahoney and Kannan Ramchandran
• "Cooperative SGD: A Unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms", Jianyu Wang and Gauri Joshi
• "Secure Coded Multi-Party Computation for Massive Matrices with Adversarial Nodes," Seyed Reza, Mohammad Ali Maddah-Ali and Mohammad Reza Aref Spotlight Talks
• Spotlight Talks Spotlight talks
• Towards a Sustainable Food Supply Chain Powered by Artificial Intelligence
• Deep Learning for Wildlife Conservation and Restoration Efforts
• Detecting anthropogenic cloud perturbations with deep learning
• Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
• Targeted Meta-Learning for Critical Incident Detection in Weather Data
• Truck Traffic Monitoring with Satellite Images
• Machine Learning for AC Optimal Power Flow
• Planetary Scale Monitoring of Urban Growth in High Flood Risk Areas Spotlights
• Spotlight Presentations Talks
• Opening Remarks
• Welcome and Introduction
• Patrick McDaniel
• Opening Remarks
• Yann LeCun
• invited talk by David Silver (Deepmind): AlphaStar: Mastering the Game of StarCraft II
• Testing Arithmetic Circuits
• invited talk by John Langford (Microsoft Research): How do we make Real World Reinforcement Learning revolution?
• Una-May O'Reilly
• Jessica Hamrick
• invited talk by Craig Boutilier (Google Research): Reinforcement Learning in Recommender Systems: Some Challenges
• DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
• Oral Paper Presentations 1
• Spotlight Session 1
• Le Song
• Stefan Schaal
• Tractable Islands Revisited
• Allen Qi
• Dream Distillation: A Data-Independent Model Compression Framework
• The State of Sparsity in Deep Neural Networks
• Sum-Product Networks and Deep Learning: A Love Marriage
• Ziko Kolter
• David Silver
• Tensor Variable Elimination in Pyro
• Oral Paper Presentations 2
• Alexander Madry
• Been Kim
• Byron Boots
• Invertible Residual Networks and a Novel Perspective on Adversarial Examples
• Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
• Single-Path NAS: Device-Aware Efficient ConvNet Design
• Chelsea Finn
• Abhinav Gupta
• Jacob Devlin
• Sven Kreiss: "Compositionality, Confidence and Crowd Modeling for Self-Driving Cars"
• Alison Gopnik
• Mayank Bansal: "ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst"
• Chiyuan Zhang: Are all layers created equal? -- Studies on how neural networks represent functions
• Chelsea Finn: "A Practical View on Generalization and Autonomy in the Real World"
• Sergey Levine: "Imitation, Prediction, and Model-Based Reinforcement Learning for Autonomous Driving"
• Wolfram Burgard
• Building a tractable generator network
• Dorsa Sadigh: "Influencing Interactive Mixed-Autonomy Systems"
• Glow: Generative Flow with Invertible 1x1 Convolutions
• Contributed talk
• Aude Oliva: Reverse engineering neuroscience and cognitive science principles
• Householder meets Sylvester: Normalizing flows for variational inference
• Yann Lecun
• Neural Ordinary Differential Equations for Continuous Normalizing Flows
• Alexander Amini: "Learning to Drive with Purpose"
• Contributed talk
• Fisher Yu: "Motion and Prediction for Autonomous Driving"
• Alfredo Canziani: "Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic "
• The Bijector API: An Invertible Function Library for TensorFlow
• Andrew Zisserman
• Jianxiong Xiao: "Self-driving Car: What we can achieve today?"
• Invertible Neural Networks for Understanding and Controlling Learned Representations
• German Ros: "Fostering Autonomous Driving Research with CARLA"
• Abhinav Gupta
• Contributed talk
• Venkatraman Narayanan: "The Promise and Challenge of ML in Self-Driving"
• Alexei Efros Tutorials
• A Primer on PAC-Bayesian Learning
• Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms
• Never-Ending Learning
• Safe Machine Learning
• Neural Approaches to Conversational AI
• Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning
• Active Learning: From Theory to Practice
• Algorithm configuration: learning in the space of algorithm designs
• A Tutorial on Attention in Deep Learning
• Active Hypothesis Testing: An Information Theoretic (re)View
• Causal Inference and Stable Learning
• Tutorial on normalizing flows Welcomes
• Welcome Welcome Remarks
• Welcome Remarks Welcoming Remarks
• Welcoming Remarks contributed talks
• Private vqSGD: Vector-Quantized Stochastic Gradient Descent
• Provable Certificates for Adversarial Examples:Fitting a Ball in the Union of Polytopes
• Theoretically Principled Trade-off between Robustness and Accuracy panels
• Panel spotlights
• Model weight theft with just noise inputs: The curious case of the petulant attacker

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