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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #1
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #2
Manifold Mixup: Better Representations by Interpolating Hidden States
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #3
Processing Megapixel Images with Deep Attention-Sampling Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #4
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #5
Online Meta-Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #6
Training Neural Networks with Local Error Signals
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #7
GMNN: Graph Markov Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #8
Self-Attention Graph Pooling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #9
Combating Label Noise in Deep Learning using Abstention
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #10
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #11
Self-Attention Generative Adversarial Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #12
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #13
High-Fidelity Image Generation With Fewer Labels
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #14
Revisiting precision recall definition for generative modeling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #15
Wasserstein of Wasserstein Loss for Learning Generative Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #16
Flat Metric Minimization with Applications in Generative Modeling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #17
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #18
Non-Parametric Priors For Generative Adversarial Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #19
Lipschitz Generative Adversarial Nets
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #20
HexaGAN: Generative Adversarial Nets for Real World Classification
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #21
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #22
BayesNAS: A Bayesian Approach for Neural Architecture Search
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #23
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #24
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #26
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #28
The Evolved Transformer
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #29
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #30
Stochastic Deep Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #31
ELF OpenGo: an analysis and open reimplementation of AlphaZero
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #32
Making Deep Q-learning methods robust to time discretization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #33
Nonlinear Distributional Gradient Temporal-Difference Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #34
Composing Entropic Policies using Divergence Correction
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #35
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #36
Multi-Agent Adversarial Inverse Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #37
Policy Consolidation for Continual Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #38
Off-Policy Deep Reinforcement Learning without Exploration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #39
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #40
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #41
An Investigation of Model-Free Planning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #42
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #43
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #44
Diagnosing Bottlenecks in Deep Q-learning Algorithms
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #45
Collaborative Evolutionary Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #46
EMI: Exploration with Mutual Information
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #47
Imitation Learning from Imperfect Demonstration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #48
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #49
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #50
Fingerprint Policy Optimisation for Robust Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #51
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #52
Differentiable Linearized ADMM
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #53
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #54
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #55
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #56
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #57
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #58
SWALP : Stochastic Weight Averaging in Low Precision Training
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #59
Efficient optimization of loops and limits with randomized telescoping sums
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #60
Self-similar Epochs: Value in arrangement
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #61
Adversarial Attacks on Node Embeddings via Graph Poisoning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #62
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #63
On Certifying Non-Uniform Bounds against Adversarial Attacks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #64
Improving Adversarial Robustness via Promoting Ensemble Diversity
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #65
Adversarial camera stickers: A physical camera-based attack on deep learning systems
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #66
Adversarial examples from computational constraints
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #67
POPQORN: Quantifying Robustness of Recurrent Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #68
Using Pre-Training Can Improve Model Robustness and Uncertainty
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #69
Generalized No Free Lunch Theorem for Adversarial Robustness
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #70
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #71
On Learning Invariant Representations for Domain Adaptation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #72
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #73
Adversarial Generation of Time-Frequency Features with application in audio synthesis
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #74
On the Universality of Invariant Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #75
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #76
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #77
Feature-Critic Networks for Heterogeneous Domain Generalization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #78
Learning to Convolve: A Generalized Weight-Tying Approach
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #79
On Dropout and Nuclear Norm Regularization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #80
Gradient Descent Finds Global Minima of Deep Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #81
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #82
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #83
Compressing Gradient Optimizers via Count-Sketches
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #84
Scalable Fair Clustering
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #85
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #86
Fault Tolerance in Iterative-Convergent Machine Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #87
Static Automatic Batching In TensorFlow
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #88
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #89
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #90
DL2: Training and Querying Neural Networks with Logic
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #91
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #92
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #93
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #94
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #95
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #96
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #97
A Composite Randomized Incremental Gradient Method
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #98
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #99
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #100
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #101
Safe Policy Improvement with Baseline Bootstrapping
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #102
Distributional Reinforcement Learning for Efficient Exploration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #103
Optimistic Policy Optimization via Multiple Importance Sampling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #104
Neural Logic Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #105
Learning to Collaborate in Markov Decision Processes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #106
Predictor-Corrector Policy Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #107
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #108
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #109
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #110
Learning from a Learner
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #111
Separable value functions across time-scales
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #112
Learning Action Representations for Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #113
Bayesian Counterfactual Risk Minimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #114
Per-Decision Option Discounting
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #115
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #116
A Theory of Regularized Markov Decision Processes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #117
Discovering Options for Exploration by Minimizing Cover Time
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #118
Policy Certificates: Towards Accountable Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #119
The Value Function Polytope in Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #120
Data Shapley: Equitable Valuation of Data for Machine Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #121
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #122
Metric-Optimized Example Weights
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #123
Improving Model Selection by Employing the Test Data
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #124
Topological Data Analysis of Decision Boundaries with Application to Model Selection
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #125
Contextual Memory Trees
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #126
Sparse Extreme Multi-label Learning with Oracle Property
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #127
Shape Constraints for Set Functions
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #128
On The Power of Curriculum Learning in Training Deep Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #129
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #130
Robust Decision Trees Against Adversarial Examples
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #131
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #132
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #133
Optimal Transport for structured data with application on graphs
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #134
Learning Optimal Linear Regularizers
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #135
On Symmetric Losses for Learning from Corrupted Labels
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #136
AUCµ: A Performance Metric for Multi-Class Machine Learning Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #137
Regularization in directable environments with application to Tetris
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #138
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #139
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #140
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #141
Collaborative Channel Pruning for Deep Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #142
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #143
GDPP: Learning Diverse Generations using Determinantal Point Processes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #144
Co-Representation Network for Generalized Zero-Shot Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #145
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #146
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #147
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #148
A Personalized Affective Memory Model for Improving Emotion Recognition
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #149
Temporal Gaussian Mixture Layer for Videos
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #150
Regret Circuits: Composability of Regret Minimizers
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #151
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #152
Stable-Predictive Optimistic Counterfactual Regret Minimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #153
When Samples Are Strategically Selected
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #154
Statistical Foundations of Virtual Democracy
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #155
Optimal Auctions through Deep Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #156
Learning to Clear the Market
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #157
Learning to bid in revenue-maximizing auctions
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #158
Open-ended learning in symmetric zero-sum games
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #159
Deep Counterfactual Regret Minimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #160
Generalized Approximate Survey Propagation for High-Dimensional Estimation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #161
Boosted Density Estimation Remastered
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #162
Inference and Sampling of $K_{33}$-free Ising Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #163
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #164
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #165
Neural Joint Source-Channel Coding
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #166
Doubly-Competitive Distribution Estimation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #167
Homomorphic Sensing
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #168
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #169
Rate Distortion For Model Compression:From Theory To Practice
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #170
Formal Privacy for Functional Data with Gaussian Perturbations
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #171
Graphical-model based estimation and inference for differential privacy
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #172
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #173
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #174
Sublinear Space Private Algorithms Under the Sliding Window Model
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #175
Locally Private Bayesian Inference for Count Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #176
Low Latency Privacy Preserving Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #177
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #178
Poission Subsampled R\'enyi Differential Privacy
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #179
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #180
Refined Complexity of PCA with Outliers
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #181
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #182
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #183
Teaching a black-box learner
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #184
PAC Learnability of Node Functions in Networked Dynamical Systems
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #185
Online learning with kernel losses
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #186
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #187
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #188
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #189
Maximum Likelihood Estimation for Learning Populations of Parameters
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #190
Projection onto Minkowski Sums with Application to Constrained Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #191
Blended Conditonal Gradients
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #192
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #193
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #194
A Conditional-Gradient-Based Augmented Lagrangian Framework
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #195
SGD: General Analysis and Improved Rates
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #196
Curvature-Exploiting Acceleration of Elastic Net Computations
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #197
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom
Safe Grid Search with Optimal Complexity
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #199
SAGA with Arbitrary Sampling
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #200
Natural Analysts in Adaptive Data Analysis
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #201
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #202
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #203
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #204
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #205
The Implicit Fairness Criterion of Unconstrained Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #206
Weak Detection of Signal in the Spiked Wigner Model
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #207
Rademacher Complexity for Adversarially Robust Generalization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #208
Provably efficient RL with Rich Observations via Latent State Decoding
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #209
Information-Theoretic Considerations in Batch Reinforcement Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #210
A Contrastive Divergence for Combining Variational Inference and MCMC
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #211
Calibrated Approximate Bayesian Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #212
Moment-Based Variational Inference for Markov Jump Processes
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #213
Understanding MCMC Dynamics as Flows on the Wasserstein Space
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #214
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #215
Amortized Monte Carlo Integration
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #216
Stein Point Markov Chain Monte Carlo
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #217
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #218
Particle Flow Bayes' Rule
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #219
Correlated Variational Auto-Encoders
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #220
Towards a Unified Analysis of Random Fourier Features
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #221
Learning deep kernels for exponential family densities
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #222
Bayesian Deconditional Kernel Mean Embeddings
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #223
A Kernel Perspective for Regularizing Deep Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #224
A Persistent Weisfeiler--Lehman Procedure for Graph Classification
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #225
Rehashing Kernel Evaluation in High Dimensions
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #226
Large-Scale Sparse Kernel Canonical Correlation Analysis
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #227
A Kernel Theory of Modern Data Augmentation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #228
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #229
Scalable Learning in Reproducing Kernel Krein Spaces
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #230
Dirichlet Simplex Nest and Geometric Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #231
Bayesian leave-one-out cross-validation for large data
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #232
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #233
Neurally-Guided Structure Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #234
Bayesian Joint Spike-and-Slab Graphical Lasso
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #235
Rotation Invariant Householder Parameterization for Bayesian PCA
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #236
A Framework for Bayesian Optimization in Embedded Subspaces
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #237
Convolutional Poisson Gamma Belief Network
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #238
Automatic Posterior Transformation for Likelihood-Free Inference
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #239
Active Learning for Decision-Making from Imbalanced Observational Data
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #240
Validating Causal Inference Models via Influence Functions
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #241
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #242
Learning to Groove with Inverse Sequence Transformations
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #243
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #244
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #245
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #246
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom
Learning to Prove Theorems via Interacting with Proof Assistants
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #248
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #249
Learning to Optimize Multigrid PDE Solvers
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #250
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #251
Learning Hawkes Processes Under Synchronization Noise
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #252
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #253
A Statistical Investigation of Long Memory in Language and Music
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #254
Deep Factors for Forecasting
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #255
Weakly-Supervised Temporal Localization via Occurrence Count Learning
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #256
Switching Linear Dynamics for Variational Bayes Filtering
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #257
Imputing Missing Events in Continuous-Time Event Streams
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #258
Understanding and Controlling Memory in Recurrent Neural Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #259
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #260
Subspace Robust Wasserstein Distances
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #261
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #262
Active Manifolds: A non-linear analogue to Active Subspaces
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #263
Optimal Minimal Margin Maximization with Boosting
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #264
Generalized Linear Rule Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #265
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #266
Variational Inference for sparse network reconstruction from count data
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #267
Simplifying Graph Convolutional Networks
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #268
Robust Influence Maximization for Hyperparametric Models
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #269
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
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Tue Jun 11 06:30 PM -- 06:50 PM (PDT) @ Pacific Ballroom #270
Rates of Convergence for Sparse Variational Gaussian Process Regression
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Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #271
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
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