Papers
Spectral Learning from a Single Trajectory under Finite-State Policies
Borja Balle, Odalric-Ambrym Maillard
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
David Balduzzi, Marcus Frean, Lennox Leary et al.
Strongly-Typed Agents are Guaranteed to Interact Safely
David Balduzzi
Differentially Private Clustering in High-Dimensional Euclidean Spaces
Maria-Florina Balcan, Travis Dick, Yingyu Liang et al.
Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms
Arturs Backurs, Christos Tzamos
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
Distributed and Provably Good Seedings for k-Means in Constant Rounds
Olivier Bachem, Mario Lucic, Andreas Krause
Uniform Deviation Bounds for k-Means Clustering
Olivier Bachem, Mario Lucic, S. Hamed Hassani et al.
Learning the Structure of Generative Models without Labeled Data
Stephen H. Bach, Bryan He, Alexander Ratner et al.
Minimax Regret Bounds for Reinforcement Learning
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco et al.
An Alternative Softmax Operator for Reinforcement Learning
Kavosh Asadi, Michael L. Littman
A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas et al.
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang et al.
Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, Léon Bottou
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
Yossi Arjevani, Ohad Shamir
Deep Voice: Real-time Neural Text-to-Speech
Sercan Ö. Arık, Mike Chrzanowski, Adam Coates et al.
A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency
Ron Appel, Pietro Perona
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
Modular Multitask Reinforcement Learning with Policy Sketches
Jacob Andreas, Dan Klein, Sergey Levine
An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation
David Anderson, Ming Gu
Input Convex Neural Networks
Brandon Amos, Lei Xu, J. Zico Kolter
OptNet: Differentiable Optimization as a Layer in Neural Networks
Brandon Amos, J. Zico Kolter
Near-Optimal Design of Experiments via Regret Minimization
Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh et al.