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Dec. 8, 2019 Expo schedule » New in ML »

Timezone: America/Vancouver »

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Time	East Exhibition Hall B	East Meeting Rooms 11 + 12	East Ballroom A	West Ballroom A + B	East Exhibition A, Ballrooms B + C	West Exhibition Hall C + B3	East Meeting Level 1, 1 + 2 + 3	East Meeting Level 1, 8 + 15	East Exhibition Hall C	West Exhibition Hall A
Mon Dec 09, 2019
08:00 AM (Affinity Workshops)							Black in AI (BAI) Affinity Workshop
08:25 AM (Affinity Workshops)									Women in Machine Learning (WiML) Affinity Workshop
08:30 AM (Tutorials)				Human Behavior Modeling with Machine Learning: Opportunities and Challenges		Imitation Learning and its Application to Natural Language Generation				Deep Learning with Bayesian Principles
08:45 AM (Affinity Workshops)			LatinX in AI (LAI) Affinity Workshop
09:00 AM (Workshops)		New In Machine Learning
11:15 AM (Tutorials)				Machine Learning for Computational Biology and Health		Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures				Interpretable Comparison of Distributions and Models
01:15 PM (Affinity Poster Sessions)	WiML + Black in AI Joint Poster Session
02:00 PM (Affinity Workshops)								Queer in AI (QAI) Affinity Workshop
02:45 PM (Tutorials)				Synthetic Control		Reinforcement Learning: Past, Present, and Future Perspectives				Representation Learning and Fairness
05:00 PM (Breaks)						Opening Remarks
05:45 PM (Invited Talks)						How to Know
06:30 PM (Affinity Poster Sessions)	Joint Affinity Group Poster Session
06:35 PM (Breaks)					Opening Reception

Time	West Exhibition Hall C + B3	West 220 - 222	West 202 - 204	West 205 - 207	West 223 + 224	West 217 - 219	West Ballroom A + B	East Exhibition Hall B + C	West Exhibition Hall A	West Ballroom C
Tue Dec 10, 2019
08:30 AM (Invited Talks)	Veridical Data Science
10:05 AM (Orals)	Track 1 Session 1 Uniform convergence may be unable to explain generalization in deep learning						Track 3 Session 1 Kernel Instrumental Variable Regression		Track 2 Session 1 Logarithmic Regret for Online Control	Track 4 Session 1 Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
10:20 AM (Spotlights)	On Exact Computation with an Infinitely Wide Neural Net						Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments		List-decodable Linear Regression	Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
10:25 AM (Spotlights)	Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks						Identification of Conditional Causal Effects under Markov Equivalence		On the Hardness of Robust Classification	Point-Voxel CNN for Efficient 3D Deep Learning
10:30 AM (Spotlights)	Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks						Likelihood-Free Overcomplete ICA and Applications In Causal Discovery		Adversarial Examples Are Not Bugs, They Are Features	Neural Networks with Cheap Differential Operators
10:35 AM (Spotlights)	Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks						Perceiving the arrow of time in autoregressive motion		Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness	Sequential Neural Processes
10:40 AM (Spotlights)	Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation						Conditional Independence Testing using Generative Adversarial Networks		Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection	Deep Equilibrium Models
10:45 AM (Posters)								Algorithms -- Adversarial Learning Algorithms -- Bandit Algorithms Algorithms -- Clustering Algorithms -- Components Analysis (e.g., CCA, ICA, LDA, PCA) Algorithms -- Density Estimation Algorithms -- Dynamical Systems Algorithms -- Kernel Methods Algorithms -- Missing Data Algorithms -- Representation Learning Algorithms -- Similarity and Distance Learning Applications -- Communication- or Memory-Bounded Learning Applications -- Dialog- or Communication-Based Learning Applications -- Game Playing Applications -- Privacy, Anonymity, and Security Applications -- Recommender Systems Applications -- Web Applications and Internet Data Deep Learning -- Biologically Plausible Deep Networks Deep Learning -- Deep Autoencoders Deep Learning -- Efficient Inference Methods Deep Learning -- Generative Models Deep Learning -- Interaction-Based Deep Networks Deep Learning -- Optimization for Deep Networks Deep Learning -- Predictive Models Deep Learning -- Recurrent Networks Deep Learning -- Visualization or Exposition Techniques for Deep Networks Optimization -- Combinatorial Optimization Probabilistic Methods -- Causal Inference Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Exploration Reinforcement Learning and Planning -- Markov Decision Processes Reinforcement Learning and Planning -- Navigation Theory -- Computational Complexity Theory -- Frequentist Statistics Theory -- Hardness of Learning and Approximations Theory -- Learning Theory
02:15 PM (Invited Talks)	Machine Learning Meets Single-Cell Biology: Insights and Challenges
03:05 PM (Invited Talks)	Test of Time
04:10 PM (Orals)	Track 1 Session 2 Generative Modeling by Estimating Gradients of the Data Distribution						Track 3 Session 2 Causal Confusion in Imitation Learning		Track 2 Session 2 Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration	Track 4 Session 2 Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
04:25 PM (Spotlights)	Learning Hierarchical Priors in VAEs						Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement		Asymmetric Valleys: Beyond Sharp and Flat Local Minima	Scalable Global Optimization via Local Bayesian Optimization
04:30 PM (Spotlights)	Implicit Generation and Modeling with Energy Based Models						Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity		Reducing the variance in online optimization by transporting past gradients	Uncertainty on Asynchronous Time Event Prediction
04:35 PM (Spotlights)	Invertible Convolutional Flow						A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning		Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models	Bayesian Optimization under Heavy-tailed Payoffs
04:40 PM (Spotlights)	Residual Flows for Invertible Generative Modeling						Learning Compositional Neural Programs with Recursive Tree Search and Planning		Fast and Provable ADMM for Learning with Generative Priors	Variational Bayesian Optimal Experimental Design
04:45 PM (Spotlights)	Dual Variational Generation for Low Shot Heterogeneous Face Recognition						Guided Meta-Policy Search		Complexity of Highly Parallel Non-Smooth Convex Optimization	Implicit Posterior Variational Inference for Deep Gaussian Processes
04:50 PM (Orals)	HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models						Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning		Necessary and Sufficient Geometries for Gradient Methods	Parameter elimination in particle Gibbs sampling
05:05 PM (Spotlights)	Adaptive Density Estimation for Generative Models						Better Exploration with Optimistic Actor Critic		SySCD: A System-Aware Parallel Coordinate Descent Algorithm	Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
05:10 PM (Spotlights)	Twin Auxilary Classifiers GAN						Robust exploration in linear quadratic reinforcement learning		Learning Positive Functions with Pseudo Mirror Descent	The Randomized Midpoint Method for Log-Concave Sampling
05:15 PM (Spotlights)	Adversarial Fisher Vectors for Unsupervised Representation Learning						Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies		UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization	Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
05:20 PM (Spotlights)	Emergence of Object Segmentation in Perturbed Generative Models						Hindsight Credit Assignment		Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm	Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
05:25 PM (Spotlights)	Compression with Flows via Local Bits-Back Coding						Weight Agnostic Neural Networks		Learning dynamic polynomial proofs	Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
05:30 PM (Demonstrations, Posters)								Demos Tue Algorithms -- Bandit Algorithms Deep Learning -- Generative Models Deep Learning -- Optimization for Deep Networks Probabilistic Methods -- Causal Inference Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Exploration Theory -- Learning Theory Algorithms -- AutoML Algorithms -- Large Scale Learning Algorithms -- Meta-Learning Algorithms -- Regression Algorithms -- Structured Prediction Algorithms -- Unsupervised Learning Applications -- Body Pose, Face, and Gesture Analysis Optimization -- Convex Optimization Optimization -- Non-Convex Optimization Optimization -- Stochastic Optimization Probabilistic Methods -- Distributed Inference Probabilistic Methods -- Gaussian Processes Probabilistic Methods -- Hierarchical Models Probabilistic Methods -- MCMC Probabilistic Methods -- Variational Inference Reinforcement Learning and Planning -- Model-Based RL Reinforcement Learning and Planning -- Multi-Agent RL Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Control Theory
07:00 PM (Socials)		RL Social @ NeurIPS 2019	Well-Being in ML	AI for Social Good	Inverse Problems Social	Learning Theory (Definitions, Theorems, Proofs, etc.)

Time	West Exhibition Hall C + B3	West 202 - 204	West 205 - 207	West 223 + 224	West 217 - 219	West Ballroom A + B	East Exhibition Hall B + C	West Exhibition Hall A	West Ballroom C
Wed Dec 11, 2019
08:30 AM (Invited Talks)	Social Intelligence
10:05 AM (Orals)	Track 1 Session 3 Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations					Track 3 Session 3 Fast and Accurate Least-Mean-Squares Solvers		Track 2 Session 3 A neurally plausible model learns successor representations in partially observable environments	Track 4 Session 3 Faster width-dependent algorithm for mixed packing and covering LPs
10:20 AM (Spotlights)	A Condition Number for Joint Optimization of Cycle-Consistent Networks					Calibration tests in multi-class classification: A unifying framework		DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections	Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
10:25 AM (Spotlights)	Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution					Verified Uncertainty Calibration		VIREL: A Variational Inference Framework for Reinforcement Learning	Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
10:30 AM (Spotlights)	Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning					Fast structure learning with modular regularization		Unsupervised Curricula for Visual Meta-Reinforcement Learning	Smoothing Structured Decomposable Circuits
10:35 AM (Spotlights)	DM2C: Deep Mixed-Modal Clustering					Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG		Policy Continuation with Hindsight Inverse Dynamics	Counting the Optimal Solutions in Graphical Models
10:40 AM (Spotlights)	Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds					PIDForest: Anomaly Detection via Partial Identification		Learning Reward Machines for Partially Observable Reinforcement Learning	Combining Generative and Discriminative Models for Hybrid Inference
10:45 AM (Posters)							Algorithms -- Adaptive Data Analysis Algorithms -- Boosting and Ensemble Methods Algorithms -- Model Selection and Structure Learning Algorithms -- Regression Algorithms -- Semi-Supervised Learning Algorithms -- Uncertainty Estimation Algorithms -- Unsupervised Learning Applications -- Computer Vision Applications -- Image Segmentation Applications -- Object Detection Applications -- Robotics Applications -- Visual Scene Analysis and Interpretation Deep Learning -- Efficient Inference Methods Deep Learning -- Generative Models Deep Learning -- Supervised Deep Networks Neuroscience and Cognitive Science -- Brain Imaging Neuroscience and Cognitive Science -- Brain Mapping Neuroscience and Cognitive Science -- Brain--Computer Interfaces and Neural Prostheses Neuroscience and Cognitive Science -- Connectomics Neuroscience and Cognitive Science -- Human or Animal Learning Neuroscience and Cognitive Science -- Language for Cognitive Science Neuroscience and Cognitive Science -- Memory Neuroscience and Cognitive Science -- Neuroscience Neuroscience and Cognitive Science -- Perception Neuroscience and Cognitive Science -- Problem Solving Neuroscience and Cognitive Science -- Visual Perception Optimization -- Stochastic Optimization Optimization -- Submodular Optimization Probabilistic Methods -- Bayesian Nonparametrics Probabilistic Methods -- Belief Propagation Probabilistic Methods -- Graphical Models Probabilistic Methods -- Latent Variable Models Probabilistic Methods -- Topic Models Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Hierarchical RL Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Game Theory and Computational Economics Theory -- Learning Theory Theory -- Regularization
02:15 PM (Invited Talks)	From System 1 Deep Learning to System 2 Deep Learning
03:50 PM (Orals)	Track 1 Session 4 Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup					Track 3 Session 4 Guided Similarity Separation for Image Retrieval		Track 2 Session 4 Batched Multi-armed Bandits Problem	Track 4 Session 4 Efficient and Thrifty Voting by Any Means Necessary
04:05 PM (Spotlights)	Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals					Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes		Are sample means in multi-armed bandits positively or negatively biased?	Finding Friend and Foe in Multi-Agent Games
04:10 PM (Spotlights)	Limitations of Lazy Training of Two-layers Neural Network					Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation		SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits	Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
04:15 PM (Spotlights)	Generalization Bounds for Neural Networks via Approximate Description Length					Efficient Meta Learning via Minibatch Proximal Update		Recovering Bandits	Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
04:20 PM (Spotlights)	Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity					Reconciling meta-learning and continual learning with online mixtures of tasks		Model Selection for Contextual Bandits	Multiagent Evaluation under Incomplete Information
04:25 PM (Orals)	On Making Stochastic Classifiers Deterministic					Geometry-Aware Neural Rendering		Strategizing against No-regret Learners	Distribution-Independent PAC Learning of Halfspaces with Massart Noise
04:40 PM (Spotlights)	Cold Case: The Lost MNIST Digits					Learning by Abstraction: The Neural State Machine		Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization	Graph-based Discriminators: Sample Complexity and Expressiveness
04:45 PM (Spotlights)	An adaptive nearest neighbor rule for classification					Heterogeneous Graph Learning for Visual Commonsense Reasoning		Learning in Generalized Linear Contextual Bandits with Stochastic Delays	Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
04:50 PM (Spotlights)	Multilabel reductions: what is my loss optimising?					Self-Critical Reasoning for Robust Visual Question Answering		Optimal Stochastic and Online Learning with Individual Iterates	McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
04:55 PM (Spotlights)	Optimal Sparse Decision Trees					SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems		Online Learning via the Differential Privacy Lens	The Broad Optimality of Profile Maximum Likelihood
05:00 PM (Demonstrations, Posters)							Demos Wed Applications -- Computer Vision Deep Learning -- Generative Models Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Game Theory and Computational Economics Theory -- Learning Theory Algorithms -- Bandit Algorithms Algorithms -- Classification Algorithms -- Collaborative Filtering Algorithms -- Few-Shot Learning Algorithms -- Meta-Learning Algorithms -- Metric Learning Algorithms -- Multitask and Transfer Learning Algorithms -- Online Learning Algorithms -- Ranking and Preference Learning Algorithms -- Relational Learning Algorithms -- Spectral Methods Applications -- Audio and Speech Processing Applications -- Information Retrieval Applications -- Matrix and Tensor Factorization Applications -- Natural Language Processing Applications -- Signal Processing Applications -- Visual Question Answering Data, Challenges, Implementations, and Software -- Benchmarks Data, Challenges, Implementations, and Software -- Data Sets or Data Repositories Data, Challenges, Implementations, and Software -- Virtual Environments Deep Learning -- Attention Models Optimization -- Convex Optimization Probabilistic Methods -- Gaussian Processes Probabilistic Methods -- Hierarchical Models Probabilistic Methods -- MCMC Probabilistic Methods -- Variational Inference Reinforcement Learning and Planning -- Multi-Agent RL Theory -- Large Deviations and Asymptotic Analysis Theory -- Statistical Physics of Learning
07:00 PM (Socials)		Find your allies: how to be an individual champion of ethical AI practices at your company & meet collaborators you need	Women in AI Ignite	T-PRIME	ML 4 Space Social

Time	West Exhibition Hall C + B3	West 118 - 120	West 220 - 222	West 202 - 204	West 205 - 207	West 223 + 224	West 217 - 219	West Ballroom A + B	East Exhibition Hall B + C	West Exhibition Hall A	West Ballroom C
Thu Dec 12, 2019
08:30 AM (Invited Talks)	Mapping Emotions: Discovering Structure in Mesoscale Electrical Brain Recordings
10:05 AM (Orals)	Track 1 Session 5 Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs							Track 3 Session 5 Exponentially convergent stochastic k-PCA without variance reduction		Track 2 Session 5 Variance Reduction for Matrix Games	Track 4 Session 5 Optimizing Generalized Rate Metrics with Three Players
10:20 AM (Spotlights)	Learning Perceptual Inference by Contrasting							Implicit Regularization in Deep Matrix Factorization		Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Modeling Conceptual Understanding in Image Reference Games
10:25 AM (Spotlights)	Universality and individuality in neural dynamics across large populations of recurrent networks							SGD on Neural Networks Learns Functions of Increasing Complexity		Adversarial Music: Real world Audio Adversary against Wake-word Detection System	This Looks Like That: Deep Learning for Interpretable Image Recognition
10:30 AM (Spotlights)	Better Transfer Learning with Inferred Successor Maps							When does label smoothing help?		Convergence of Adversarial Training in Overparametrized Neural Networks	Assessing Social and Intersectional Biases in Contextualized Word Representations
10:35 AM (Spotlights)	A unified theory for the origin of grid cells through the lens of pattern formation							Splitting Steepest Descent for Growing Neural Architectures		Adversarial Training and Robustness for Multiple Perturbations	Paradoxes in Fair Machine Learning
10:40 AM (Spotlights)	Infra-slow brain dynamics as a marker for cognitive function and decline							Positional Normalization		Zero-shot Knowledge Transfer via Adversarial Belief Matching	Multi-Criteria Dimensionality Reduction with Applications to Fairness
10:45 AM (Posters)									Algorithms -- Active Learning Algorithms -- Adversarial Learning Algorithms -- AutoML Algorithms -- Bandit Algorithms Algorithms -- Classification Algorithms -- Multitask and Transfer Learning Algorithms -- Representation Learning Applications -- Fairness, Accountability, and Transparency Applications -- Privacy, Anonymity, and Security Applications -- Quantitative Finance and Econometrics Applications -- Time Series Analysis Deep Learning -- Adversarial Networks Deep Learning -- CNN Architectures Deep Learning -- Efficient Training Methods Deep Learning -- Optimization for Deep Networks Neuroscience and Cognitive Science -- Cognitive Science Neuroscience and Cognitive Science -- Neural Coding Neuroscience and Cognitive Science -- Neuroscience Neuroscience and Cognitive Science -- Reasoning Optimization -- Non-Convex Optimization Optimization -- Stochastic Optimization Reinforcement Learning and Planning -- Planning Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Information Theory Theory -- Regularization
01:00 PM (Breaks, Talks)		Accelerating Deep Learning At Wafer Scale By Cerebras Systems									Town Hall and Business Meeting
02:15 PM (Invited Talks)	Agency + Automation: Designing Artificial Intelligence into Interactive Systems
03:50 PM (Orals)	Track 1 Session 6 Average Individual Fairness: Algorithms, Generalization and Experiments							Track 3 Session 6 Blind Super-Resolution Kernel Estimation using an Internal-GAN		Track 2 Session 6 Putting An End to End-to-End: Gradient-Isolated Learning of Representations	Track 4 Session 6 Understanding Sparse JL for Feature Hashing
04:05 PM (Spotlights)	Ask not what AI can do, but what AI should do: Towards a framework of task delegability							Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance		On the Downstream Performance of Compressed Word Embeddings	A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
04:10 PM (Spotlights)	Making AI Forget You: Data Deletion in Machine Learning							Theoretical Analysis of Adversarial Learning: A Minimax Approach		CPM-Nets: Cross Partial Multi-View Networks	Efficiently Learning Fourier Sparse Set Functions
04:15 PM (Spotlights)	On Testing for Biases in Peer Review							Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem		Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models	Generalization Error Analysis of Quantized Compressive Learning
04:20 PM (Spotlights)	A Step Toward Quantifying Independently Reproducible Machine Learning Research							Differentiable Ranking and Sorting using Optimal Transport		Large Memory Layers with Product Keys	Surfing: Iterative Optimization Over Incrementally Trained Deep Networks
04:25 PM (Orals)	On Robustness of Principal Component Regression							Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses		XLNet: Generalized Autoregressive Pretraining for Language Understanding	R2D2: Reliable and Repeatable Detector and Descriptor
04:40 PM (Spotlights)	Private Learning Implies Online Learning: An Efficient Reduction							KerGM: Kernelized Graph Matching		Cross-lingual Language Model Pretraining	Quadratic Video Interpolation
04:45 PM (Spotlights)	Private Stochastic Convex Optimization with Optimal Rates							Wasserstein Weisfeiler-Lehman Graph Kernels		N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules	Training Image Estimators without Image Ground Truth
04:50 PM (Spotlights)	Practical Differentially Private Top-k Selection with Pay-what-you-get Composition							Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel		Evaluating Protein Transfer Learning with TAPE	STREETS: A Novel Camera Network Dataset for Traffic Flow
04:55 PM (Spotlights)	Differentially Private Markov Chain Monte Carlo							Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing		Cormorant: Covariant Molecular Neural Networks	Reflection Separation using a Pair of Unpolarized and Polarized Images
05:00 PM (Posters)									Algorithms -- Representation Learning Applications -- Fairness, Accountability, and Transparency Applications -- Privacy, Anonymity, and Security Deep Learning -- Optimization for Deep Networks Optimization -- Non-Convex Optimization Algorithms -- Components Analysis (e.g., CCA, ICA, LDA, PCA) Algorithms -- Kernel Methods Algorithms -- Nonlinear Dimensionality Reduction and Manifold Learning Algorithms -- Online Learning Algorithms -- Sparse Coding and Dimensionality Expansion Algorithms -- Sparsity and Compressed Sensing Algorithms -- Stochastic Methods Algorithms -- Structured Prediction Algorithms -- Unsupervised Learning Applications -- Activity and Event Recognition Applications -- Computational Biology and Bioinformatics Applications -- Computational Photography Applications -- Computational Social Science Applications -- Computer Vision Applications -- Denoising Applications -- Hardware and Systems Applications -- Health Applications -- Natural Language Processing Applications -- Network Analysis Applications -- Object Recognition Applications -- Program Understanding and Generation Applications -- Sustainability Applications -- Tracking and Motion in Video Applications -- Video Analysis Data, Challenges, Implementations, and Software -- Data Sets or Data Repositories Data, Challenges, Implementations, and Software -- Software Toolkits Deep Learning -- Embedding Approaches Deep Learning -- Memory-Augmented Neural Networks Theory -- Frequentist Statistics Theory -- Game Theory and Computational Economics Theory -- Large Deviations and Asymptotic Analysis Theory -- Learning Theory Theory -- Spaces of Functions and Kernels Deep Learning -- Predictive Models
07:00 PM (Socials)			{Dis}Ability in AI	Social Event for Budding Researchers	Human-aligned AI	Deep Learning Researchers and Start-Ups - Working at Start-Ups vs Large Companies and How to Fix Diversity Issues at Start-Ups	The Great NeurIPS Debate 2019 Social

Time	West Exhibition Hall C	East Exhibition Hall A	West Ballroom A	West Ballroom B	West 211 - 214	West 301 - 305	East Ballroom A	East Ballroom B	East Ballroom C	West 109 + 110	West 118 - 120	West 220 - 222	East Meeting Rooms 11 + 12	West 202 - 204	West 205 - 207	East Meeting Rooms 8 + 15	West 121 + 122	West 223 + 224	West 306	West 217 - 219	West 208 + 209	West 215 + 216	West 114 + 115	West 116 + 117	East Meeting Rooms 1 - 3	West Exhibition Hall A	West Ballroom C
Fri Dec 13, 2019
08:00 AM (Workshops)	Bayesian Deep Learning	Information Theory and Machine Learning	Machine Learning for Health (ML4H): What makes machine learning in medicine different?	Meta-Learning	Beyond first order methods in machine learning systems	Solving inverse problems with deep networks: New architectures, theoretical foundations, and applications	Safety and Robustness in Decision-making	Learning Meaningful Representations of Life	Optimal Transport for Machine Learning	KR2ML - Knowledge Representation and Reasoning Meets Machine Learning	Workshop on Federated Learning for Data Privacy and Confidentiality	Shared Visual Representations in Human and Machine Intelligence	MLSys: Workshop on Systems for ML	Visually Grounded Interaction and Language	Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy	Minding the Gap: Between Fairness and Ethics	Machine Learning for the Developing World (ML4D): Challenges and Risks	Workshop on Human-Centric Machine Learning	EMC2: Energy Efficient Machine Learning and Cognitive Computing (5th edition)	AI for Humanitarian Assistance and Disaster Response	Learning with Rich Experience: Integration of Learning Paradigms	CiML 2019: Machine Learning Competitions for All	Retrospectives: A Venue for Self-Reflection in ML Research	Competition Track Day 1	Perception as generative reasoning: structure, causality, probability	Graph Representation Learning	Biological and Artificial Reinforcement Learning

Time	East Exhibition Hall C	West Ballroom A	West Ballroom B	West 211 - 214	West 301 - 305	East Ballroom A	East Ballroom B	East Ballroom C	West 109 + 110	West 118 - 120	West 220 - 222	East Meeting Rooms 11 + 12	West 202 - 204	West 205 - 207	East Meeting Rooms 8 + 15	West 121 + 122	West 223 + 224	West 306	West 217 - 219	West 208 + 209	West 215 + 216	West 114 + 115	West 116 + 117	East Meeting Rooms 1 - 3	West Exhibition Hall C	West Exhibition Hall A	West Ballroom C
Sat Dec 14, 2019
08:00 AM (Workshops)		The Optimization Foundations of Reinforcement Learning	Machine Learning with Guarantees	Learning Transferable Skills	Medical Imaging meets NeurIPS	Real Neurons & Hidden Units: future directions at the intersection of neuroscience and AI	Fair ML in Healthcare	Tackling Climate Change with ML	Machine Learning and the Physical Sciences	Emergent Communication: Towards Natural Language	Robot Learning: Control and Interaction in the Real World	Joint Workshop on AI for Social Good	ML For Systems	The third Conversational AI workshop – today's practice and tomorrow's potential	Privacy in Machine Learning (PriML)	Science meets Engineering of Deep Learning	NeurIPS Workshop on Machine Learning for Creativity and Design 3.0	Learning with Temporal Point Processes	Context and Compositionality in Biological and Artificial Neural Systems	Document Intelligence	Sets and Partitions	Program Transformations for ML	Competition Track Day 2	Machine Learning for Autonomous Driving	Deep Reinforcement Learning	Bridging Game Theory and Deep Learning	“Do the right thing”: machine learning and causal inference for improved decision making
06:00 PM (Breaks)	Closing Reception + Live Music																							Closing Reception (Quiet Space)
07:00 PM (Socials)											Probabilistic Programming Social

Mon Dec 09, 2019

Tue Dec 10, 2019

Wed Dec 11, 2019

Thu Dec 12, 2019

Fri Dec 13, 2019

Sat Dec 14, 2019