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June 9, 2019 Expo schedule » Black in AI, LatinX in AI, Queer in AI, and WiML Events » New in ML »
Indicates the beginning of a session

 Thu Jun 13, 2019

Time Pacific Ballroom
06:30 PM (Posters)
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations Pacific Ballroom #1
Variational Laplace Autoencoders Pacific Ballroom #2
Latent Normalizing Flows for Discrete Sequences Pacific Ballroom #3
Multi-objective training of Generative Adversarial Networks with multiple discriminators Pacific Ballroom #4
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement Pacific Ballroom #5
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables Pacific Ballroom #6
Graphite: Iterative Generative Modeling of Graphs Pacific Ballroom #7
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets Pacific Ballroom #9
On Scalable and Efficient Computation of Large Scale Optimal Transport Pacific Ballroom #10
Understanding and correcting pathologies in the training of learned optimizers Pacific Ballroom #11
Demystifying Dropout Pacific Ballroom #12
Ladder Capsule Network Pacific Ballroom #13
Unreproducible Research is Reproducible Pacific Ballroom #14
Geometric Scattering for Graph Data Analysis Pacific Ballroom #15
Robust Inference via Generative Classifiers for Handling Noisy Labels Pacific Ballroom #16
LIT: Learned Intermediate Representation Training for Model Compression Pacific Ballroom #17
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Pacific Ballroom #18
What is the Effect of Importance Weighting in Deep Learning? Pacific Ballroom #19
Similarity of Neural Network Representations Revisited Pacific Ballroom #20
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations Pacific Ballroom #21
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures Pacific Ballroom #22
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities Pacific Ballroom #23
Multi-Object Representation Learning with Iterative Variational Inference Pacific Ballroom #24
Cross-Domain 3D Equivariant Image Embeddings Pacific Ballroom #25
Loss Landscapes of Regularized Linear Autoencoders Pacific Ballroom #26
Hyperbolic Disk Embeddings for Directed Acyclic Graphs Pacific Ballroom #27
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition Pacific Ballroom #28
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness Pacific Ballroom #29
Lorentzian Distance Learning for Hyperbolic Representations Pacific Ballroom #30
Batch Policy Learning under Constraints Pacific Ballroom #31
Quantifying Generalization in Reinforcement Learning Pacific Ballroom #32
Learning Latent Dynamics for Planning from Pixels Pacific Ballroom #33
Projections for Approximate Policy Iteration Algorithms Pacific Ballroom #34
Learning Structured Decision Problems with Unawareness Pacific Ballroom #35
Calibrated Model-Based Deep Reinforcement Learning Pacific Ballroom #36
Reinforcement Learning in Configurable Continuous Environments Pacific Ballroom #37
Target-Based Temporal-Difference Learning Pacific Ballroom #38
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees Pacific Ballroom #39
Finding Options that Minimize Planning Time Pacific Ballroom #40
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement Pacific Ballroom #41
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs Pacific Ballroom #42
Meta-Learning Neural Bloom Filters Pacific Ballroom #43
CoT: Cooperative Training for Generative Modeling of Discrete Data Pacific Ballroom #44
Non-Monotonic Sequential Text Generation Pacific Ballroom #45
Insertion Transformer: Flexible Sequence Generation via Insertion Operations Pacific Ballroom #46
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models Pacific Ballroom #47
Trainable Decoding of Sets of Sequences for Neural Sequence Models Pacific Ballroom #48
Learning to Generalize from Sparse and Underspecified Rewards Pacific Ballroom #49
Efficient Training of BERT by Progressively Stacking Pacific Ballroom #50
Decentralized Exploration in Multi-Armed Bandits Pacific Ballroom #51
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback Pacific Ballroom #52
Exploiting structure of uncertainty for efficient matroid semi-bandits Pacific Ballroom #53
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits Pacific Ballroom #54
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model Pacific Ballroom #55
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning Pacific Ballroom #56
TarMAC: Targeted Multi-Agent Communication Pacific Ballroom #57
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning Pacific Ballroom #58
Actor-Attention-Critic for Multi-Agent Reinforcement Learning Pacific Ballroom #59
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning Pacific Ballroom #60
Neural Network Attributions: A Causal Perspective Pacific Ballroom #61
Towards a Deep and Unified Understanding of Deep Neural Models in NLP Pacific Ballroom #62
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation Pacific Ballroom #63
Functional Transparency for Structured Data: a Game-Theoretic Approach Pacific Ballroom #64
Exploring interpretable LSTM neural networks over multi-variable data Pacific Ballroom #65
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing Pacific Ballroom #66
Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute Pacific Ballroom #67
State-Regularized Recurrent Neural Networks Pacific Ballroom #68
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Pacific Ballroom #69
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Pacific Ballroom #70
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem Pacific Ballroom #71
On the Spectral Bias of Neural Networks Pacific Ballroom #72
Recursive Sketches for Modular Deep Learning Pacific Ballroom #73
Zero-Shot Knowledge Distillation in Deep Networks Pacific Ballroom #74
A Convergence Theory for Deep Learning via Over-Parameterization Pacific Ballroom #75
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks Pacific Ballroom #76
Approximation and non-parametric estimation of ResNet-type convolutional neural networks Pacific Ballroom #77
Global Convergence of Block Coordinate Descent in Deep Learning Pacific Ballroom #78
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians Pacific Ballroom #79
On the Limitations of Representing Functions on Sets Pacific Ballroom #80
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering Pacific Ballroom #81
Nonparametric Bayesian Deep Networks with Local Competition Pacific Ballroom #82
Good Initializations of Variational Bayes for Deep Models Pacific Ballroom #83
Dropout as a Structured Shrinkage Prior Pacific Ballroom #84
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables Pacific Ballroom #85
On Variational Bounds of Mutual Information Pacific Ballroom #86
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation Pacific Ballroom #87
Hierarchical Importance Weighted Autoencoders Pacific Ballroom #88
Faster Attend-Infer-Repeat with Tractable Probabilistic Models Pacific Ballroom #89
Understanding Priors in Bayesian Neural Networks at the Unit Level Pacific Ballroom #90
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning Pacific Ballroom #91
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization Pacific Ballroom #92
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically Pacific Ballroom #93
Width Provably Matters in Optimization for Deep Linear Neural Networks Pacific Ballroom #94
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? Pacific Ballroom #95
Power k-Means Clustering Pacific Ballroom #96
Distributed Learning over Unreliable Networks Pacific Ballroom #97
Escaping Saddle Points with Adaptive Gradient Methods Pacific Ballroom #98
$\texttt{DoubleSqueeze}$: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression Pacific Ballroom #99
Model Function Based Conditional Gradient Method with Armijo-like Line Search Pacific Ballroom #100
Analogies Explained: Towards Understanding Word Embeddings Pacific Ballroom #101
Parameter-Efficient Transfer Learning for NLP Pacific Ballroom #102
Efficient On-Device Models using Neural Projections Pacific Ballroom #103
Deep Residual Output Layers for Neural Language Generation Pacific Ballroom #104
Improving Neural Language Modeling via Adversarial Training Pacific Ballroom #105
Mixture Models for Diverse Machine Translation: Tricks of the Trade Pacific Ballroom #106
MASS: Masked Sequence to Sequence Pre-training for Language Generation Pacific Ballroom #107
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops Pacific Ballroom #108
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization Pacific Ballroom #109
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network Pacific Ballroom #110
COMIC: Multi-view Clustering Without Parameter Selection Pacific Ballroom #111
The Wasserstein Transform Pacific Ballroom #112
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm Pacific Ballroom #113
Neural Collaborative Subspace Clustering Pacific Ballroom #114
Unsupervised Deep Learning by Neighbourhood Discovery Pacific Ballroom #115
Autoregressive Energy Machines Pacific Ballroom #116
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization Pacific Ballroom #117
Noise2Self: Blind Denoising by Self-Supervision Pacific Ballroom #118
Learning Dependency Structures for Weak Supervision Models Pacific Ballroom #119
Geometry and Symmetry in Short-and-Sparse Deconvolution Pacific Ballroom #120
On Sparse Linear Regression in the Local Differential Privacy Model Pacific Ballroom #121
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions Pacific Ballroom #122
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy Pacific Ballroom #123
Differentially Private Learning of Geometric Concepts Pacific Ballroom #124
Toward Controlling Discrimination in Online Ad Auctions Pacific Ballroom #125
Learning Optimal Fair Policies Pacific Ballroom #126
Fairness-Aware Learning for Continuous Attributes and Treatments Pacific Ballroom #127
Fairness risk measures Pacific Ballroom #128
Proportionally Fair Clustering Pacific Ballroom #129
Stable and Fair Classification Pacific Ballroom #130
Flexibly Fair Representation Learning by Disentanglement Pacific Ballroom #131
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms Pacific Ballroom #132
Fairness without Harm: Decoupled Classifiers with Preference Guarantees Pacific Ballroom #133
Differentially Private Fair Learning Pacific Ballroom #134
Obtaining Fairness using Optimal Transport Theory Pacific Ballroom #135
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Pacific Ballroom #136
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning Pacific Ballroom #137
Making Decisions that Reduce Discriminatory Impacts Pacific Ballroom #138
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications Pacific Ballroom #139
Online Algorithms for Rent-Or-Buy with Expert Advice Pacific Ballroom #140
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity Pacific Ballroom #141
Categorical Feature Compression via Submodular Optimization Pacific Ballroom #142
Multi-Frequency Phase Synchronization Pacific Ballroom #143
Faster Algorithms for Binary Matrix Factorization Pacific Ballroom #144
Guided evolutionary strategies: augmenting random search with surrogate gradients Pacific Ballroom #146
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces Pacific Ballroom #147
Semi-Cyclic Stochastic Gradient Descent Pacific Ballroom #148
Matrix-Free Preconditioning in Online Learning Pacific Ballroom #149
Online Convex Optimization in Adversarial Markov Decision Processes Pacific Ballroom #150
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games Pacific Ballroom #151
Online Learning with Sleeping Experts and Feedback Graphs Pacific Ballroom #152
Incremental Randomized Sketching for Online Kernel Learning Pacific Ballroom #153
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models Pacific Ballroom #154
Online Control with Adversarial Disturbances Pacific Ballroom #155
Adversarial Online Learning with noise Pacific Ballroom #156
Online Variance Reduction with Mixtures Pacific Ballroom #157
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case Pacific Ballroom #158
Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret Pacific Ballroom #159
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems Pacific Ballroom #160
Adaptive Regret of Convex and Smooth Functions Pacific Ballroom #161
Online Adaptive Principal Component Analysis and Its extensions Pacific Ballroom #162
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction Pacific Ballroom #163
Anytime Online-to-Batch, Optimism and Acceleration Pacific Ballroom #164
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints Pacific Ballroom #165
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning Pacific Ballroom #166
Adaptive Sensor Placement for Continuous Spaces Pacific Ballroom #167
Scale-free adaptive planning for deterministic dynamics & discounted rewards Pacific Ballroom #168
Communication-Constrained Inference and the Role of Shared Randomness Pacific Ballroom #169
Learning and Data Selection in Big Datasets Pacific Ballroom #170
Sublinear quantum algorithms for training linear and kernel-based classifiers Pacific Ballroom #171
Agnostic Federated Learning Pacific Ballroom #172
Discovering Conditionally Salient Features with Statistical Guarantees Pacific Ballroom #173
A Theoretical Analysis of Contrastive Unsupervised Representation Learning Pacific Ballroom #174
The information-theoretic value of unlabeled data in semi-supervised learning Pacific Ballroom #175
Unsupervised Label Noise Modeling and Loss Correction Pacific Ballroom #176
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment Pacific Ballroom #177
Pareto Optimal Streaming Unsupervised Classification Pacific Ballroom #178
Geometric Losses for Distributional Learning Pacific Ballroom #179
Classification from Positive, Unlabeled and Biased Negative Data Pacific Ballroom #180
Complementary-Label Learning for Arbitrary Losses and Models Pacific Ballroom #181
Learning to Infer Program Sketches Pacific Ballroom #182
Hierarchically Structured Meta-learning Pacific Ballroom #183
Bridging Theory and Algorithm for Domain Adaptation Pacific Ballroom #184
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation Pacific Ballroom #185
Learning What and Where to Transfer Pacific Ballroom #186
DBSCAN++: Towards fast and scalable density clustering Pacific Ballroom #187
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction Pacific Ballroom #188
Gromov-Wasserstein Learning for Graph Matching and Node Embedding Pacific Ballroom #189
Spectral Clustering of Signed Graphs via Matrix Power Means Pacific Ballroom #190
Coresets for Ordered Weighted Clustering Pacific Ballroom #191
Fair k-Center Clustering for Data Summarization Pacific Ballroom #192
A Better k-means++ Algorithm via Local Search Pacific Ballroom #193
Kernel Normalized Cut: a Theoretical Revisit Pacific Ballroom #194
Guarantees for Spectral Clustering with Fairness Constraints Pacific Ballroom #195
Supervised Hierarchical Clustering with Exponential Linkage Pacific Ballroom #196
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions Pacific Ballroom #197
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization Pacific Ballroom #198
Unifying Orthogonal Monte Carlo Methods Pacific Ballroom #199
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits Pacific Ballroom #200
Metropolis-Hastings Generative Adversarial Networks Pacific Ballroom #201
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets Pacific Ballroom #202
Replica Conditional Sequential Monte Carlo Pacific Ballroom #203
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes Pacific Ballroom #204
Adaptive Antithetic Sampling for Variance Reduction Pacific Ballroom #205
Accelerated Flow for Probability Distributions Pacific Ballroom #206
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering Pacific Ballroom #207
Dimensionality Reduction for Tukey Regression Pacific Ballroom #208
Efficient Full-Matrix Adaptive Regularization Pacific Ballroom #209
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms Pacific Ballroom #210
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations Pacific Ballroom #211
Robust Estimation of Tree Structured Gaussian Graphical Models Pacific Ballroom #212
Spectral Approximate Inference Pacific Ballroom #213
Partially Linear Additive Gaussian Graphical Models Pacific Ballroom #214
DAG-GNN: DAG Structure Learning with Graph Neural Networks Pacific Ballroom #215
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights Pacific Ballroom #216
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random Pacific Ballroom #217
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations Pacific Ballroom #218
Model Comparison for Semantic Grouping Pacific Ballroom #219
RaFM: Rank-Aware Factorization Machines Pacific Ballroom #220
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning Pacific Ballroom #221
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement Pacific Ballroom #222
Neural Separation of Observed and Unobserved Distributions Pacific Ballroom #223
Almost Unsupervised Text to Speech and Automatic Speech Recognition Pacific Ballroom #224
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss Pacific Ballroom #225
A fully differentiable beam search decoder Pacific Ballroom #226
Scaling Up Ordinal Embedding: A Landmark Approach Pacific Ballroom #227
Learning to select for a predefined ranking Pacific Ballroom #228
Mallows ranking models: maximum likelihood estimate and regeneration Pacific Ballroom #229
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models Pacific Ballroom #230
Fast Algorithm for Generalized Multinomial Models with Ranking Data Pacific Ballroom #231
Graph Resistance and Learning from Pairwise Comparisons Pacific Ballroom #232
Learning Context-dependent Label Permutations for Multi-label Classification Pacific Ballroom #233
Discovering Context Effects from Raw Choice Data Pacific Ballroom #234
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference Pacific Ballroom #235
Learning Distance for Sequences by Learning a Ground Metric Pacific Ballroom #236