NeurIPS 2019

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

Timezone: America/Vancouver »

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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.)

Tue Dec 10, 2019