Papers
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.
Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng, Qi Meng, Taifeng Wang et al.
Attentive Recurrent Comparators
Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
Jayadev Acharya, Hirakendu Das, Alon Orlitsky et al.
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu
A Unified View of Multi-Label Performance Measures
Xi-Zhu Wu, Zhi-Hua Zhou
Automated Curriculum Learning for Neural Networks
Alex Graves, Marc G. Bellemare, Jacob Menick et al.
Automatic Discovery of the Statistical Types of Variables in a Dataset
Isabel Valera, Zoubin Ghahramani
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, Qiqi Yan
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Zi Wang, Chengtao Li, Stefanie Jegelka et al.
Bayesian Boolean Matrix Factorisation
Tammo Rukat, Chris C. Holmes, Michalis K. Titsias et al.
Bayesian inference on random simple graphs with power law degree distributions
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani et al.
Bayesian Models of Data Streams with Hierarchical Power Priors
Andrés Masegosa, Thomas D. Nielsen, Helge Langseth et al.
Bayesian Optimization with Tree-structured Dependencies
Rodolphe Jenatton, Cedric Archambeau, Javier González et al.
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane et al.
Beyond Filters: Compact Feature Map for Portable Deep Model
Yunhe Wang, Chang Xu, Chao Xu et al.