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Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ Victoria
Decoupled Parallel Backpropagation with Convergence Guarantee
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A9
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ K1 + K2
Essentially No Barriers in Neural Network Energy Landscape
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A5
Dependent Relational Gamma Process Models for Longitudinal Networks
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ K11
Learning to Optimize Combinatorial Functions
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A7
Differentiable Abstract Interpretation for Provably Robust Neural Networks
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Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A1
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A6
Delayed Impact of Fair Machine Learning
Oral
Thu Jul 12 12:00 AM -- 12:20 AM (KST) @ A4
Yes, but Did It Work?: Evaluating Variational Inference
Oral
Thu Jul 12 12:20 AM -- 12:30 AM (KST) @ A9
Fast Variance Reduction Method with Stochastic Batch Size
Oral
Thu Jul 12 12:20 AM -- 12:30 AM (KST) @ K1 + K2
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A7
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
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Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A5
NetGAN: Generating Graphs via Random Walks
Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A4
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A1
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A3
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
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Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ A6
Fairness Without Demographics in Repeated Loss Minimization
Oral
Thu Jul 12 12:20 AM -- 12:30 AM (KST) @ K11
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Oral
Thu Jul 12 12:20 AM -- 12:40 AM (KST) @ Victoria
Efficient Neural Architecture Search via Parameters Sharing
Oral
Thu Jul 12 12:30 AM -- 12:40 AM (KST) @ A9
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Oral
Thu Jul 12 12:30 AM -- 12:40 AM (KST) @ K11
Binary Partitions with Approximate Minimum Impurity
Oral
Thu Jul 12 12:30 AM -- 12:40 AM (KST) @ K1 + K2
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A9
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ K1 + K2
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A1
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ K11
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ Victoria
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A6
Nonconvex Optimization for Regression with Fairness Constraints
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A3
Been There, Done That: Meta-Learning with Episodic Recall
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Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A4
Black-Box Variational Inference for Stochastic Differential Equations
Oral
Thu Jul 12 12:40 AM -- 12:50 AM (KST) @ A5
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A9
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ K1 + K2
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A7
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
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Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A3
Continual Reinforcement Learning with Complex Synapses
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Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ Victoria
Spline Filters For End-to-End Deep Learning
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Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A4
Inference Suboptimality in Variational Autoencoders
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ K11
Bounds on the Approximation Power of Feedforward Neural Networks
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A5
Neural Relational Inference for Interacting Systems
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A1
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Oral
Thu Jul 12 12:50 AM -- 01:00 AM (KST) @ A6
Fair and Diverse DPP-Based Data Summarization
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #1
Spline Filters For End-to-End Deep Learning
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #2
Non-linear motor control by local learning in spiking neural networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #3
Implicit Quantile Networks for Distributional Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #4
An Inference-Based Policy Gradient Method for Learning Options
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #6
Differentially Private Matrix Completion Revisited
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #7
Differentiable plasticity: training plastic neural networks with backpropagation
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #8
Model-Level Dual Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #9
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #10
Tree Edit Distance Learning via Adaptive Symbol Embeddings
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #11
Gradually Updated Neural Networks for Large-Scale Image Recognition
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #12
One-Shot Segmentation in Clutter
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #13
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #14
Learning Deep ResNet Blocks Sequentially using Boosting Theory
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #15
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #16
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #17
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #18
Subspace Embedding and Linear Regression with Orlicz Norm
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #19
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #20
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #21
Learning the Reward Function for a Misspecified Model
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #22
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #23
Do Outliers Ruin Collaboration?
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #24
Dropout Training, Data-dependent Regularization, and Generalization Bounds
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #25
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #26
Continual Reinforcement Learning with Complex Synapses
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #27
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #28
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #29
Learning Diffusion using Hyperparameters
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #30
Learning a Mixture of Two Multinomial Logits
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #31
Crowdsourcing with Arbitrary Adversaries
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #32
Deep Density Destructors
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #33
Programmatically Interpretable Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #34
Structured Evolution with Compact Architectures for Scalable Policy Optimization
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #35
The Weighted Kendall and High-order Kernels for Permutations
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #36
The Limits of Maxing, Ranking, and Preference Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #38
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #39
Clustering Semi-Random Mixtures of Gaussians
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #40
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #41
Learning by Playing - Solving Sparse Reward Tasks from Scratch
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #42
Structured Control Nets for Deep Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #43
Stagewise Safe Bayesian Optimization with Gaussian Processes
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #44
Bayesian Optimization of Combinatorial Structures
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #45
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #46
Dependent Relational Gamma Process Models for Longitudinal Networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #47
K-means clustering using random matrix sparsification
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #48
Hierarchical Clustering with Structural Constraints
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #49
Kronecker Recurrent Units
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #50
Semi-Supervised Learning via Compact Latent Space Clustering
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #51
Dynamic Evaluation of Neural Sequence Models
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #52
TACO: Learning Task Decomposition via Temporal Alignment for Control
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #53
A Spectral Approach to Gradient Estimation for Implicit Distributions
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #54
Quasi-Monte Carlo Variational Inference
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #55
Learning to Optimize Combinatorial Functions
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #56
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #57
Representation Learning on Graphs with Jumping Knowledge Networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #58
NetGAN: Generating Graphs via Random Walks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #59
INSPECTRE: Privately Estimating the Unseen
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #60
Locally Private Hypothesis Testing
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #61
Latent Space Policies for Hierarchical Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #62
More Robust Doubly Robust Off-policy Evaluation
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #63
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #64
End-to-end Active Object Tracking via Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #65
Efficient and Consistent Adversarial Bipartite Matching
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #66
SparseMAP: Differentiable Sparse Structured Inference
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #67
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #68
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #69
Parameterized Algorithms for the Matrix Completion Problem
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #70
Nearly Optimal Robust Subspace Tracking
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #71
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #72
signSGD: Compressed Optimisation for Non-Convex Problems
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #73
Synthesizing Robust Adversarial Examples
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #74
Differentiable Abstract Interpretation for Provably Robust Neural Networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #75
Stochastic Training of Graph Convolutional Networks with Variance Reduction
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #76
Neural Relational Inference for Interacting Systems
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #77
Which Training Methods for GANs do actually Converge?
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #78
Learning Independent Causal Mechanisms
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #79
Nonconvex Optimization for Regression with Fairness Constraints
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #80
Fairness Without Demographics in Repeated Loss Minimization
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #81
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #82
Nonoverlap-Promoting Variable Selection
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #83
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #84
Graph Networks as Learnable Physics Engines for Inference and Control
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #85
An Alternative View: When Does SGD Escape Local Minima?
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #86
Asynchronous Decentralized Parallel Stochastic Gradient Descent
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #87
An Estimation and Analysis Framework for the Rasch Model
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #88
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #89
Local Private Hypothesis Testing: Chi-Square Tests
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #90
Disentangling by Factorising
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #91
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #92
Learning to search with MCTSnets
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #93
Decoupled Parallel Backpropagation with Convergence Guarantee
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #94
On Learning Sparsely Used Dictionaries from Incomplete Samples
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #95
Variational Network Inference: Strong and Stable with Concrete Support
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #96
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #97
Data Summarization at Scale: A Two-Stage Submodular Approach
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #98
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #99
Learning with Abandonment
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #100
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #101
Generative Temporal Models with Spatial Memory for Partially Observed Environments
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #102
DiCE: The Infinitely Differentiable Monte Carlo Estimator
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #103
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #104
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #105
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #106
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #107
Coordinated Exploration in Concurrent Reinforcement Learning
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #108
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #109
Learning Steady-States of Iterative Algorithms over Graphs
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #110
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #111
Fair and Diverse DPP-Based Data Summarization
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #112
Learning Implicit Generative Models with the Method of Learned Moments
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #113
Chi-square Generative Adversarial Network
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Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #114
Streaming Principal Component Analysis in Noisy Setting
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #115
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #116
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #117
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #118
Stability and Generalization of Learning Algorithms that Converge to Global Optima
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #119
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #120
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #121
Fast Parametric Learning with Activation Memorization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #122
Essentially No Barriers in Neural Network Energy Landscape
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #123
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #124
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #125
Bayesian Quadrature for Multiple Related Integrals
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #126
Deep Predictive Coding Network for Object Recognition
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #127
Neural Inverse Rendering for General Reflectance Photometric Stereo
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #128
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #129
Selecting Representative Examples for Program Synthesis
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #130
Conditional Neural Processes
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #131
Hierarchical Long-term Video Prediction without Supervision
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #132
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #133
A Classification-Based Study of Covariate Shift in GAN Distributions
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #134
Gated Path Planning Networks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #135
Automatic Goal Generation for Reinforcement Learning Agents
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #136
ADMM and Accelerated ADMM as Continuous Dynamical Systems
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #137
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #138
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #139
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #140
Fast Variance Reduction Method with Stochastic Batch Size
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #141
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #142
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #143
The Well-Tempered Lasso
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #144
Transfer Learning via Learning to Transfer
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #145
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #146
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #147
Deep One-Class Classification
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #148
Binary Partitions with Approximate Minimum Impurity
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #149
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #150
Yes, but Did It Work?: Evaluating Variational Inference
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #151
Black-Box Variational Inference for Stochastic Differential Equations
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #152
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #153
Learning to Speed Up Structured Output Prediction
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #154
Differentially Private Identity and Equivalence Testing of Discrete Distributions
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #155
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #156
BOCK : Bayesian Optimization with Cylindrical Kernels
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #157
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #158
Distributed Nonparametric Regression under Communication Constraints
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #159
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #160
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #161
Safe Element Screening for Submodular Function Minimization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #162
Feedback-Based Tree Search for Reinforcement Learning
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #163
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #164
Data-Dependent Stability of Stochastic Gradient Descent
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #165
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #166
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #167
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #168
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #169
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #170
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #171
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #172
Path Consistency Learning in Tsallis Entropy Regularized MDPs
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #173
Lipschitz Continuity in Model-based Reinforcement Learning
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #174___1
Linear Spectral Estimators and an Application to Phase Retrieval
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #174___0
Bounds on the Approximation Power of Feedforward Neural Networks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #175
Testing Sparsity over Known and Unknown Bases
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #176
Inference Suboptimality in Variational Autoencoders
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #177
Semi-Implicit Variational Inference
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #178
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #179
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #180
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #181
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #182
An Efficient Semismooth Newton based Algorithm for Convex Clustering
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #183
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #184
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #185
Efficient Neural Architecture Search via Parameters Sharing
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #186
Non-convex Conditional Gradient Sliding
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #187
Stochastic Variance-Reduced Cubic Regularized Newton Method
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #188
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #189
The Dynamics of Learning: A Random Matrix Approach
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #190
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #191
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #192
Continuous-Time Flows for Efficient Inference and Density Estimation
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #193
Tighter Variational Bounds are Not Necessarily Better
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #194
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #195
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #196
Differentiable Compositional Kernel Learning for Gaussian Processes
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #197
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #198
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #199
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #200
Anonymous Walk Embeddings
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #201
Improving Optimization in Models With Continuous Symmetry Breaking
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #202
Conditional Noise-Contrastive Estimation of Unnormalised Models
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #203
Canonical Tensor Decomposition for Knowledge Base Completion
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #204
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
In
Posters Wed
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #205
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #206
Escaping Saddles with Stochastic Gradients
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #207
$D^2$: Decentralized Training over Decentralized Data
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #208
Machine Theory of Mind
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #209
Been There, Done That: Meta-Learning with Episodic Recall
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #210
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #211
Coded Sparse Matrix Multiplication
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #212
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #213
Efficient Gradient-Free Variational Inference using Policy Search
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #214
Fixing a Broken ELBO
In
Posters Wed
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #215
Variational Inference and Model Selection with Generalized Evidence Bounds
In
Posters Wed
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PDF]
Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #216
The Generalization Error of Dictionary Learning with Moreau Envelopes
In
Posters Wed
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Poster
Thu Jul 12 01:15 AM -- 04:00 AM (KST) @ Hall B #217
Network Global Testing by Counting Graphlets
In
Posters Wed
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PDF]
Break
Thu Jul 12 02:30 AM -- 03:00 AM (KST) @ Hall B
Coffee Break
Break
Thu Jul 12 04:00 AM -- 05:30 AM (KST)
Lunch - on your own
Break
Thu Jul 12 07:30 AM -- 08:00 AM (KST) @ Hall B
Coffee Break
Break
Thu Jul 12 10:15 AM -- 11:15 AM (KST) @ Hall B
Light Evening Snack
Session
Thu Jul 12 05:00 PM -- 05:20 PM (KST) @ A1
Best Paper Session 2
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A6
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A7
Geometry Score: A Method For Comparing Generative Adversarial Networks
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A5
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A4
Learning unknown ODE models with Gaussian processes
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ K1
Learning Memory Access Patterns
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A9
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
[
PDF]
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ K11
Probabilistic Boolean Tensor Decomposition
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ Victoria
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
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PDF]
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A3
Learning Policy Representations in Multiagent Systems
Oral
Thu Jul 12 06:00 PM -- 06:20 PM (KST) @ A1
Convergent Tree Backup and Retrace with Function Approximation
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ Victoria
Compressing Neural Networks using the Variational Information Bottelneck
[
PDF]
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ A9
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
[
PDF]
Oral
Thu Jul 12 06:20 PM -- 06:30 PM (KST) @ A7
Optimizing the Latent Space of Generative Networks
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ A4
Constraining the Dynamics of Deep Probabilistic Models
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ A5
Differentiable Dynamic Programming for Structured Prediction and Attention
Oral
Thu Jul 12 06:20 PM -- 06:30 PM (KST) @ A3
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Oral
Thu Jul 12 06:20 PM -- 06:30 PM (KST) @ A6
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ A1
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Oral
Thu Jul 12 06:20 PM -- 06:40 PM (KST) @ K1
Geodesic Convolutional Shape Optimization
Oral
Thu Jul 12 06:20 PM -- 06:30 PM (KST) @ K11
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Oral
Thu Jul 12 06:30 PM -- 06:40 PM (KST) @ K11
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Oral
Thu Jul 12 06:30 PM -- 06:40 PM (KST) @ A7
Adversarial Learning with Local Coordinate Coding
Oral
Thu Jul 12 06:30 PM -- 06:40 PM (KST) @ A6
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Oral
Thu Jul 12 06:30 PM -- 06:40 PM (KST) @ A3
Learning to Act in Decentralized Partially Observable MDPs
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A4
Probabilistic Recurrent State-Space Models
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ K11
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ K1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A7
Learning Representations and Generative Models for 3D Point Clouds
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A9
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
[
PDF]
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A6
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A1
Scalable Bilinear Pi Learning Using State and Action Features
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A3
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Oral
Thu Jul 12 06:40 PM -- 06:50 PM (KST) @ A5
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A6
Adversarial Regression with Multiple Learners
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A4
Structured Variationally Auto-encoded Optimization
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A9
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
[
PDF]
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A7
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A3
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ K11
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ K1
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ Victoria
Deep Models of Interactions Across Sets
[
PDF]
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A5
End-to-End Learning for the Deep Multivariate Probit Model
Oral
Thu Jul 12 06:50 PM -- 07:00 PM (KST) @ A1
Stochastic Variance-Reduced Policy Gradient
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A5
Accelerated Spectral Ranking
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A6
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A7
Composite Functional Gradient Learning of Generative Adversarial Models
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A4
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ Victoria
Focused Hierarchical RNNs for Conditional Sequence Processing
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Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ K1
Learning One Convolutional Layer with Overlapping Patches
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A9
Shampoo: Preconditioned Stochastic Tensor Optimization
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Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A3
Fast Information-theoretic Bayesian Optimisation
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ A1
Investigating Human Priors for Playing Video Games
Oral
Thu Jul 12 08:30 PM -- 08:50 PM (KST) @ K11
Improved large-scale graph learning through ridge spectral sparsification
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A6
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A4
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A5
Composite Marginal Likelihood Methods for Random Utility Models
Oral
Thu Jul 12 08:50 PM -- 09:10 PM (KST) @ Victoria
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
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Oral
Thu Jul 12 08:50 PM -- 09:10 PM (KST) @ K1
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A9
Characterizing Implicit Bias in Terms of Optimization Geometry
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Oral
Thu Jul 12 08:50 PM -- 09:10 PM (KST) @ A3
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A1
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Oral
Thu Jul 12 08:50 PM -- 09:00 PM (KST) @ A7
Tempered Adversarial Networks
Oral
Thu Jul 12 08:50 PM -- 09:10 PM (KST) @ K11
Parallel and Streaming Algorithms for K-Core Decomposition
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A1
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A4
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A7
Improved Training of Generative Adversarial Networks Using Representative Features
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A6
Prediction Rule Reshaping
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A5
Ranking Distributions based on Noisy Sorting
Oral
Thu Jul 12 09:00 PM -- 09:10 PM (KST) @ A9
A Distributed Second-Order Algorithm You Can Trust
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Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ K1
The Multilinear Structure of ReLU Networks
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A9
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
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Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A7
A Two-Step Computation of the Exact GAN Wasserstein Distance
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A6
Finding Influential Training Samples for Gradient Boosted Decision Trees
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ K11
Fast Approximate Spectral Clustering for Dynamic Networks
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A4
A Robust Approach to Sequential Information Theoretic Planning
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A5
SQL-Rank: A Listwise Approach to Collaborative Ranking
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A3
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ A1
Time Limits in Reinforcement Learning
Oral
Thu Jul 12 09:10 PM -- 09:20 PM (KST) @ Victoria
Learning long term dependencies via Fourier recurrent units
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Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A3
Tight Regret Bounds for Bayesian Optimization in One Dimension
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ K1
Understanding the Loss Surface of Neural Networks for Binary Classification
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A5
Extreme Learning to Rank via Low Rank Assumption
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A6
Noise2Noise: Learning Image Restoration without Clean Data
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A7
Is Generator Conditioning Causally Related to GAN Performance?
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ K11
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A4
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ Victoria
Training Neural Machines with Trace-Based Supervision
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Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A1
Visualizing and Understanding Atari Agents
Oral
Thu Jul 12 09:20 PM -- 09:30 PM (KST) @ A9
Gradient Coding from Cyclic MDS Codes and Expander Graphs
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Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ K11
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A3
To Understand Deep Learning We Need to Understand Kernel Learning
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ K1
Tropical Geometry of Deep Neural Networks
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A5
Feasible Arm Identification
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A1
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A4
Robust and Scalable Models of Microbiome Dynamics
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ Victoria
Neural Dynamic Programming for Musical Self Similarity
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Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A9
Alternating Randomized Block Coordinate Descent
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Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A7
Black-box Adversarial Attacks with Limited Queries and Information
Oral
Thu Jul 12 09:30 PM -- 09:50 PM (KST) @ A6
Dimensionality-Driven Learning with Noisy Labels
Oral
Thu Jul 12 09:50 PM -- 10:10 PM (KST) @ A1
Smoothed Action Value Functions for Learning Gaussian Policies
Oral
Thu Jul 12 09:50 PM -- 10:10 PM (KST) @ A7
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
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Oral
Thu Jul 12 09:50 PM -- 10:00 PM (KST) @ Victoria
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
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Oral
Thu Jul 12 09:50 PM -- 10:10 PM (KST) @ A3
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Oral
Thu Jul 12 09:50 PM -- 10:00 PM (KST) @ A6
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Oral
Thu Jul 12 09:50 PM -- 10:00 PM (KST) @ A4
Stein Variational Message Passing for Continuous Graphical Models
Oral
Thu Jul 12 09:50 PM -- 10:10 PM (KST) @ K1
A Spline Theory of Deep Learning
Oral
Thu Jul 12 09:50 PM -- 10:00 PM (KST) @ K11
Loss Decomposition for Fast Learning in Large Output Spaces
Oral
Thu Jul 12 09:50 PM -- 10:10 PM (KST) @ A5
Bandits with Delayed, Aggregated Anonymous Feedback
Oral
Thu Jul 12 10:00 PM -- 10:10 PM (KST) @ A6
Learning to Reweight Examples for Robust Deep Learning
Oral
Thu Jul 12 10:00 PM -- 10:10 PM (KST) @ A4
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Oral
Thu Jul 12 10:00 PM -- 10:10 PM (KST) @ K11
Ultra Large-Scale Feature Selection using Count-Sketches
Oral
Thu Jul 12 10:00 PM -- 10:10 PM (KST) @ Victoria
Fast Decoding in Sequence Models Using Discrete Latent Variables
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Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A1
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
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Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ Victoria
PixelSNAIL: An Improved Autoregressive Generative Model
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Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A6
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A4
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A5
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ K1
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A3
Differentially Private Database Release via Kernel Mean Embeddings
Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ A9
Accelerating Greedy Coordinate Descent Methods
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Oral
Thu Jul 12 10:10 PM -- 10:20 PM (KST) @ K11
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A4
Bucket Renormalization for Approximate Inference
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A3
Learning in Reproducing Kernel Kreı̆n Spaces
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A9
On Acceleration with Noise-Corrupted Gradients
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Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A1
Addressing Function Approximation Error in Actor-Critic Methods
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ K1
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A6
Improving Regression Performance with Distributional Losses
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A7
GAIN: Missing Data Imputation using Generative Adversarial Nets
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Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ K11
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Oral
Thu Jul 12 10:20 PM -- 10:30 PM (KST) @ A5
Thompson Sampling for Combinatorial Semi-Bandits
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A4
Variational Bayesian dropout: pitfalls and fixes
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ K1
Reviving and Improving Recurrent Back-Propagation
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A7
The Mechanics of n-Player Differentiable Games
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Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A3
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ K11
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A6
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A9
Approximate message passing for amplitude based optimization
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Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ Victoria
Using Inherent Structures to design Lean 2-layer RBMs
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Oral
Thu Jul 12 11:00 PM -- 11:20 PM (KST) @ A5
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Oral
Thu Jul 12 11:20 PM -- 11:30 PM (KST) @ K1
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Oral
Thu Jul 12 11:20 PM -- 11:30 PM (KST) @ Victoria
Deep Asymmetric Multi-task Feature Learning
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Oral
Thu Jul 12 11:20 PM -- 11:40 PM (KST) @ A1
Beyond the One-Step Greedy Approach in Reinforcement Learning
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Oral
Thu Jul 12 11:20 PM -- 11:30 PM (KST) @ A7
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
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Oral
Thu Jul 12 11:20 PM -- 11:40 PM (KST) @ A6
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Oral
Thu Jul 12 11:20 PM -- 11:30 PM (KST) @ A4
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Oral
Thu Jul 12 11:20 PM -- 11:30 PM (KST) @ K11
Approximation Guarantees for Adaptive Sampling
Oral
Thu Jul 12 11:20 PM -- 11:40 PM (KST) @ A9
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
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Oral
Thu Jul 12 11:20 PM -- 11:40 PM (KST) @ A3
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Oral
Thu Jul 12 11:20 PM -- 11:40 PM (KST) @ A5
Practical Contextual Bandits with Regression Oracles
Oral
Thu Jul 12 11:30 PM -- 11:40 PM (KST) @ K1
Invariance of Weight Distributions in Rectified MLPs
Oral
Thu Jul 12 11:30 PM -- 11:40 PM (KST) @ Victoria
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
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Oral
Thu Jul 12 11:30 PM -- 11:40 PM (KST) @ A4
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Oral
Thu Jul 12 11:30 PM -- 11:40 PM (KST) @ K11
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
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Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A6
Functional Gradient Boosting based on Residual Network Perception
Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ K1
Learning Dynamics of Linear Denoising Autoencoders
Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A5
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ Victoria
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
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Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A9
prDeep: Robust Phase Retrieval with a Flexible Deep Network
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Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A3
Fitting New Speakers Based on a Short Untranscribed Sample
Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A4
Scalable approximate Bayesian inference for particle tracking data
Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A1
Policy and Value Transfer in Lifelong Reinforcement Learning
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Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ A7
Towards Fast Computation of Certified Robustness for ReLU Networks
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Oral
Thu Jul 12 11:40 PM -- 11:50 PM (KST) @ K11
Constrained Interacting Submodular Groupings
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ Victoria
High Performance Zero-Memory Overhead Direct Convolutions
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Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A1
Importance Weighted Transfer of Samples in Reinforcement Learning
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Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A3
Towards Binary-Valued Gates for Robust LSTM Training
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A9
Accelerating Natural Gradient with Higher-Order Invariance
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Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A5
Stochastic Proximal Algorithms for AUC Maximization
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ K11
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A4
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ K1
Understanding Generalization and Optimization Performance of Deep CNNs
Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A7
LaVAN: Localized and Visible Adversarial Noise
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Oral
Thu Jul 12 11:50 PM -- 12:00 AM (KST) @ A6
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
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