papers
Publications (13)
stat.ML2016
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Kirthevasan Kandasamy, Yaoliang Yu
cs.LG2019
Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu
cs.LG2017
Convex-constrained Sparse Additive Modeling and Its Extensions
Junming Yin, Yaoliang Yu
cs.LG2017
Dropout with Expectation-linear Regularization
Xuezhe Ma, Yingkai Gao, Zhiting Hu +3
math.OC2014
Generalized Conditional Gradient for Sparse Estimation
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
cs.LG2012
Analysis of Kernel Mean Matching under Covariate Shift
Yaoliang Yu, Csaba Szepesvari
cs.LG2019
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao +3
cs.LG2015
Distributed Machine Learning via Sufficient Factor Broadcasting
Pengtao Xie, Jin Kyu Kim, Yi Zhou +4
cs.LG2019
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
Kaiwen Wu, Yaoliang Yu
math.OC2017
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
Yi Zhou, Yaoliang Yu, Wei Dai +2
cs.LG2012
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
Yaoliang Yu, James Neufeld, Ryan Kiros +2
cs.LG2018
Provably noise-robust, regularised $k$-means clustering
Shrinu Kushagra, Yaoliang Yu, Shai Ben-David
cs.LG2015
Distributed Machine Learning via Sufficient Factor Broadcasting
Pengtao Xie, Jin Kyu Kim, Yi Zhou +4