Papers
Adaptive Sampling Probabilities for Non-Smooth Optimization
Hongseok Namkoong, Aman Sinha, Steve Yadlowsky et al.
A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan et al.
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
A Laplacian Framework for Option Discovery in Reinforcement Learning
Marlos C. Machado, Marc G. Bellemare, Michael Bowling
Algebraic Variety Models for High-Rank Matrix Completion
Greg Ongie, Rebecca Willett, Robert D. Nowak et al.
Algorithmic Stability and Hypothesis Complexity
Tongliang Liu, Gábor Lugosi, Gergely Neu et al.
Algorithms for $\ell_p$ Low-Rank Approximation
Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar et al.
An Adaptive Test of Independence with Analytic Kernel Embeddings
Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton
Analogical Inference for Multi-relational Embeddings
Hanxiao Liu, Yuexin Wu, Yiming Yang
An Alternative Softmax Operator for Reinforcement Learning
Kavosh Asadi, Michael L. Littman
Analysis and Optimization of Graph Decompositions by Lifted Multicuts
Andrea Horňáková, Jan-Hendrik Lange, Bjoern Andres
Analytical Guarantees on Numerical Precision of Deep Neural Networks
Charbel Sakr, Yongjune Kim, Naresh Shanbhag
An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation
David Anderson, Ming Gu
An Infinite Hidden Markov Model With Similarity-Biased Transitions
Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison
Approximate Newton Methods and Their Local Convergence
Haishan Ye, Luo Luo, Zhihua Zhang
Approximate Steepest Coordinate Descent
Sebastian U. Stich, Anant Raj, Martin Jaggi
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
Tianbao Yang, Qihang Lin, Lijun Zhang
A Semismooth Newton Method for Fast, Generic Convex Programming
Alnur Ali, Eric Wong, J. Zico Kolter
A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency
Ron Appel, Pietro Perona
A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
Jianbo Ye, James Z. Wang, Jia Li
Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Asynchronous Distributed Variational Gaussian Process for Regression
Hao Peng, Shandian Zhe, Xiao Zhang et al.