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June 9, 2019 Expo schedule » New in ML »

Timezone: America/Los_Angeles »

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Time	Room 201	Pacific Ballroom	Hall A	Grand Ballroom	Room 102	Seaside Ballroom	Room 101	Room 103	Room 104	Hall B
Tue Jun 11, 2019
08:45 AM (Talks)			Opening Remarks
09:00 AM (Invited Talks)			Machine learning for robots to think fast
10:00 AM (Invited Talks, Orals)			Best Paper Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
11:00 AM (Orals)	Applications Validating Causal Inference Models via Influence Functions		Deep Learning Algorithms SelectiveNet: A Deep Neural Network with an Integrated Reject Option	Adversarial Examples Adversarial Attacks on Node Embeddings via Graph Poisoning	Learning Theory: Games Regret Circuits: Composability of Regret Minimizers	Supervised Learning Data Shapley: Equitable Valuation of Data for Machine Learning	Approximate Inference A Contrastive Divergence for Combining Variational Inference and MCMC	Learning Theory Refined Complexity of PCA with Outliers	Non-convex Optimization PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization	Deep RL 1 ELF OpenGo: an analysis and open reimplementation of AlphaZero
11:20 AM (Orals)	Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks		Manifold Mixup: Better Representations by Interpolating Hidden States	First-Order Adversarial Vulnerability of Neural Networks and Input Dimension	Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function	Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data	Calibrated Approximate Bayesian Inference	On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms	Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization	Making Deep Q-learning methods robust to time discretization
11:25 AM (Orals)	Learning to Groove with Inverse Sequence Transformations		Processing Megapixel Images with Deep Attention-Sampling Models	On Certifying Non-Uniform Bounds against Adversarial Attacks	Stable-Predictive Optimistic Counterfactual Regret Minimization	Metric-Optimized Example Weights	Moment-Based Variational Inference for Markov Jump Processes	Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models	Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization	Nonlinear Distributional Gradient Temporal-Difference Learning
11:30 AM (Orals)	Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI		TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning	Improving Adversarial Robustness via Promoting Ensemble Diversity	When Samples Are Strategically Selected	Improving Model Selection by Employing the Test Data	Understanding MCMC Dynamics as Flows on the Wasserstein Space	Teaching a black-box learner	Lower Bounds for Smooth Nonconvex Finite-Sum Optimization	Composing Entropic Policies using Divergence Correction
11:35 AM (Orals)	HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving		Online Meta-Learning	Adversarial camera stickers: A physical camera-based attack on deep learning systems	Statistical Foundations of Virtual Democracy	Topological Data Analysis of Decision Boundaries with Application to Model Selection	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations	PAC Learnability of Node Functions in Networked Dynamical Systems	Nonconvex Variance Reduced Optimization with Arbitrary Sampling	TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
11:40 AM (Orals)	Molecular Hypergraph Grammar with Its Application to Molecular Optimization		Training Neural Networks with Local Error Signals	Adversarial examples from computational constraints	Optimal Auctions through Deep Learning	Contextual Memory Trees	Amortized Monte Carlo Integration	Online learning with kernel losses	Error Feedback Fixes SignSGD and other Gradient Compression Schemes	Multi-Agent Adversarial Inverse Reinforcement Learning
12:00 PM (Orals)	Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance		GMNN: Graph Markov Neural Networks	POPQORN: Quantifying Robustness of Recurrent Neural Networks	Learning to Clear the Market	Sparse Extreme Multi-label Learning with Oracle Property	Stein Point Markov Chain Monte Carlo	Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates	A Composite Randomized Incremental Gradient Method	Policy Consolidation for Continual Reinforcement Learning
12:05 PM (Orals)	Learning to Prove Theorems via Interacting with Proof Assistants		Self-Attention Graph Pooling	Using Pre-Training Can Improve Model Robustness and Uncertainty	Learning to bid in revenue-maximizing auctions	Shape Constraints for Set Functions	Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations	Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise	Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference	Off-Policy Deep Reinforcement Learning without Exploration
12:10 PM (Orals)	Circuit-GNN: Graph Neural Networks for Distributed Circuit Design		Combating Label Noise in Deep Learning using Abstention		Open-ended learning in symmetric zero-sum games	On The Power of Curriculum Learning in Training Deep Networks	Particle Flow Bayes' Rule	Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension	Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always	Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
12:15 PM (Orals)	Learning to Optimize Multigrid PDE Solvers		LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning	PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach	Deep Counterfactual Regret Minimization	Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration	Correlated Variational Auto-Encoders	Maximum Likelihood Estimation for Learning Populations of Parameters	Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number	Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
02:00 PM (Orals)	Time Series A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation		Generative Adversarial Networks Self-Attention Generative Adversarial Networks	Deep Learning Theory On Learning Invariant Representations for Domain Adaptation	Information Theory and Estimation Generalized Approximate Survey Propagation for High-Dimensional Estimation	Supervised Learning Robust Decision Trees Against Adversarial Examples	Kernel Methods Towards a Unified Analysis of Random Fourier Features	Convex Optimization Projection onto Minkowski Sums with Application to Constrained Learning	Reinforcement Learning Theory Safe Policy Improvement with Baseline Bootstrapping	Deep RL 2 An Investigation of Model-Free Planning
02:20 PM (Orals)	Learning Hawkes Processes Under Synchronization Noise		Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching	Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models	Boosted Density Estimation Remastered	Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth	Learning deep kernels for exponential family densities	Blended Conditonal Gradients	Distributional Reinforcement Learning for Efficient Exploration	CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
02:25 PM (Orals)	Generative Adversarial User Model for Reinforcement Learning Based Recommendation System		High-Fidelity Image Generation With Fewer Labels	Adversarial Generation of Time-Frequency Features with application in audio synthesis	Inference and Sampling of $K_{33}$-free Ising Models	Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation	Bayesian Deconditional Kernel Mean Embeddings	Acceleration of SVRG and Katyusha X by Inexact Preconditioning	Optimistic Policy Optimization via Multiple Importance Sampling	Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
02:30 PM (Orals)	A Statistical Investigation of Long Memory in Language and Music		Revisiting precision recall definition for generative modeling	On the Universality of Invariant Networks	Random Matrix Improved Covariance Estimation for a Large Class of Metrics	Optimal Transport for structured data with application on graphs	A Kernel Perspective for Regularizing Deep Neural Networks	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD	Neural Logic Reinforcement Learning	Diagnosing Bottlenecks in Deep Q-learning Algorithms
02:35 PM (Orals)	Deep Factors for Forecasting		Wasserstein of Wasserstein Loss for Learning Generative Models	Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks	Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication	Learning Optimal Linear Regularizers	A Persistent Weisfeiler--Lehman Procedure for Graph Classification	A Conditional-Gradient-Based Augmented Lagrangian Framework	Learning to Collaborate in Markov Decision Processes	Collaborative Evolutionary Reinforcement Learning
02:40 PM (Orals)	Weakly-Supervised Temporal Localization via Occurrence Count Learning		Flat Metric Minimization with Applications in Generative Modeling	Gauge Equivariant Convolutional Networks and the Icosahedral CNN	Neural Joint Source-Channel Coding	On Symmetric Losses for Learning from Corrupted Labels	Rehashing Kernel Evaluation in High Dimensions	SGD: General Analysis and Improved Rates	Predictor-Corrector Policy Optimization	EMI: Exploration with Mutual Information
03:00 PM (Orals)	Switching Linear Dynamics for Variational Bayes Filtering		Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs	Feature-Critic Networks for Heterogeneous Domain Generalization	Doubly-Competitive Distribution Estimation	AUCµ: A Performance Metric for Multi-Class Machine Learning Models	Large-Scale Sparse Kernel Canonical Correlation Analysis	Curvature-Exploiting Acceleration of Elastic Net Computations	Learning a Prior over Intent via Meta-Inverse Reinforcement Learning	Imitation Learning from Imperfect Demonstration
03:05 PM (Orals)	Imputing Missing Events in Continuous-Time Event Streams		Non-Parametric Priors For Generative Adversarial Networks	Learning to Convolve: A Generalized Weight-Tying Approach	Homomorphic Sensing	Regularization in directable environments with application to Tetris	A Kernel Theory of Modern Data Augmentation	Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication	DeepMDP: Learning Continuous Latent Space Models for Representation Learning	Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
03:10 PM (Orals)	Understanding and Controlling Memory in Recurrent Neural Networks		Lipschitz Generative Adversarial Nets	On Dropout and Nuclear Norm Regularization	Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements	Improved Dynamic Graph Learning through Fault-Tolerant Sparsification	kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection	Safe Grid Search with Optimal Complexity	Importance Sampling Policy Evaluation with an Estimated Behavior Policy	Dynamic Weights in Multi-Objective Deep Reinforcement Learning
03:15 PM (Orals)	Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces		HexaGAN: Generative Adversarial Nets for Real World Classification	Gradient Descent Finds Global Minima of Deep Neural Networks	Rate Distortion For Model Compression:From Theory To Practice	Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin	Scalable Learning in Reproducing Kernel Krein Spaces	SAGA with Arbitrary Sampling	Learning from a Learner	Fingerprint Policy Optimisation for Robust Reinforcement Learning
04:00 PM (Orals)	General ML Subspace Robust Wasserstein Distances		Deep Learning Architectures Graph Matching Networks for Learning the Similarity of Graph Structured Objects	Large Scale Learning and Systems Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm	Privacy Formal Privacy for Functional Data with Gaussian Perturbations	Applications: Computer Vision Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff	Bayesian Methods Dirichlet Simplex Nest and Geometric Inference	Statistical Learning Theory Natural Analysts in Adaptive Data Analysis	Reinforcement Learning Theory Separable value functions across time-scales	Deep Learning Optimization An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
04:20 PM (Orals)	Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models		BayesNAS: A Bayesian Approach for Neural Architecture Search	Sublinear Time Nearest Neighbor Search over Generalized Weighted Space	Graphical-model based estimation and inference for differential privacy	Collaborative Channel Pruning for Deep Networks	Bayesian leave-one-out cross-validation for large data	CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration	Learning Action Representations for Reinforcement Learning	Differentiable Linearized ADMM
04:25 PM (Orals)	Active Manifolds: A non-linear analogue to Active Subspaces		Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks	Compressing Gradient Optimizers via Count-Sketches	White-box vs Black-box: Bayes Optimal Strategies for Membership Inference	Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization	Rao-Blackwellized Stochastic Gradients for Discrete Distributions	Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction	Bayesian Counterfactual Risk Minimization	Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
04:30 PM (Orals)	Optimal Minimal Margin Maximization with Boosting		Shallow-Deep Networks: Understanding and Mitigating Network Overthinking	Scalable Fair Clustering	An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule	GDPP: Learning Diverse Generations using Determinantal Point Processes	Neurally-Guided Structure Inference	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	Per-Decision Option Discounting	A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
04:35 PM (Orals)	Generalized Linear Rule Models		Graph U-Nets	Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator	Sublinear Space Private Algorithms Under the Sliding Window Model	Co-Representation Network for Generalized Zero-Shot Learning	Bayesian Joint Spike-and-Slab Graphical Lasso	Optimality Implies Kernel Sum Classifiers are Statistically Efficient	Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds	The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
04:40 PM (Orals)	Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications		SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver	Fault Tolerance in Iterative-Convergent Machine Learning	Locally Private Bayesian Inference for Count Models	GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects	Rotation Invariant Householder Parameterization for Bayesian PCA	The Implicit Fairness Criterion of Unconstrained Learning	A Theory of Regularized Markov Decision Processes	AdaGrad stepsizes: sharp convergence over nonconvex landscapes
05:00 PM (Orals)	Variational Inference for sparse network reconstruction from count data		Area Attention	Static Automatic Batching In TensorFlow	Low Latency Privacy Preserving Inference	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	A Framework for Bayesian Optimization in Embedded Subspaces	Weak Detection of Signal in the Spiked Wigner Model	Discovering Options for Exploration by Minimizing Cover Time	Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
05:05 PM (Orals)	Simplifying Graph Convolutional Networks		The Evolved Transformer	Improving Neural Network Quantization without Retraining using Outlier Channel Splitting	Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters	Geometry Aware Convolutional Filters for Omnidirectional Images Representation	Convolutional Poisson Gamma Belief Network	Rademacher Complexity for Adversarially Robust Generalization	Policy Certificates: Towards Accountable Reinforcement Learning	SWALP : Stochastic Weight Averaging in Low Precision Training
05:10 PM (Orals)	Robust Influence Maximization for Hyperparametric Models		Jumpout : Improved Dropout for Deep Neural Networks with ReLUs	Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications	Poission Subsampled R\'enyi Differential Privacy	A Personalized Affective Memory Model for Improving Emotion Recognition	Automatic Posterior Transformation for Likelihood-Free Inference	Provably efficient RL with Rich Observations via Latent State Decoding	Action Robust Reinforcement Learning and Applications in Continuous Control	Efficient optimization of loops and limits with randomized telescoping sums
05:15 PM (Orals)	HyperGAN: A Generative Model for Diverse, Performant Neural Networks		Stochastic Deep Networks	DL2: Training and Querying Neural Networks with Logic	Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA	Temporal Gaussian Mixture Layer for Videos	Active Learning for Decision-Making from Imbalanced Observational Data	Information-Theoretic Considerations in Batch Reinforcement Learning	The Value Function Polytope in Reinforcement Learning	Self-similar Epochs: Value in arrangement
06:30 PM (Posters)		Posters Tue

Tue Jun 11, 2019