5 citations · 13 across the 4 of their papers we have counts for
6 papers
Pre-processing with Orthogonal Decompositions for High-dimensional Explanatory Variables
Xu Han, Ethan X Fang, Cheng Yong Tang
Strong correlations between explanatory variables are problematic for high-dimensional regularized regression methods. Due to the violation of the Irrepresentable Condition, the po…
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
Yining Wang, Yi Chen, Ethan X. Fang +2
We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action ass…
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Yan Li, Ethan X. Fang, Huan Xu +1
Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unex…
High-dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong
In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional…
Multi-Level Stochastic Gradient Methods for Nested Composition Optimization
Shuoguang Yang, Mengdi Wang, Ethan X. Fang
Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions. Existing results mainly apply to optim…
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Zhuoran Yang, Lin F. Yang, Ethan X. Fang +3
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models. To address this issue, we ta…