Ethan Xingyuang Fang

Assistant Professor of Statistics

Ethan Xingyuang Fang

Publication Tags

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Chemical Analysis Testing Optimization Gradient Methods High Dimensional Confidence Interval Optimization Problem Test Statistic Randomized Trial Descent Longitudinal Data Proportional Hazards Model Obesity Hypothesis Testing Linear Programming Fats Fairness Trend Bandit Problems Hazard Function O Glcnac Transferase Numerical Experiment Estimator Type I Error Robustness

Most Recent Publications

High-dimensional Interactions Detection with Sparse Principal Hessian Matrix

Cheng Yong Tang, Xingyuan Fang, Yuexiao Dong, Journal of Machine Learning Research

Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming

Michael Rosenblum, Xingyuan Fang, H Liu, Journal of Royal Statistical Society: Series B

Deep Spatial Q-Learning for Infectious Disease Control

Zhishuai Liu, Jesse Clifton, Eric B. Laber, John Drake, Ethan X. Fang, 2023, Journal of Agricultural, Biological, and Environmental Statistics on p. 749-773

Robust matrix estimations meet Frank–Wolfe algorithm

Naimin Jing, Ethan X. Fang, Cheng Yong Tang, 2023, Machine Learning on p. 2723-2760

Lagrangian Inference for Ranking Problems

Yue Liu, Ethan X. Fang, Junwei Lu, 2023, Operations Research on p. 202-223

PASTA: Pessimistic Assortment Optimization

Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh, 2023, Proceedings of Machine Learning Research on p. 8276-8295

Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection

Yi Chen, Yining Wang, Ethan X. Fang, Zhaoran Wang, Runze Li, 2022, Journal of the American Statistical Association

Fairness-Oriented Learning for Optimal Individualized Treatment Rules

Ethan X. Fang, Zhaoran Wang, Lan Wang, 2022, Journal of the American Statistical Association

Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming

Michael Rosenblum, Ethan X. Fang, Han Liu, 2020, Journal of the Royal Statistical Society. Series B: Statistical Methodology on p. 749-772

IMPLICIT BIAS OF GRADIENT DESCENT BASED ADVERSARIAL TRAINING ON SEPARABLE DATA

Yan Li, Huan Xu, Tuo Zhao, Ethan X. Fang, 2020,

Most-Cited Papers

Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions

Mengdi Wang, Ethan X. Fang, Han Liu, 2017, Mathematical Programming on p. 419-449

Generalized alternating direction method of multipliers: new theoretical insights and applications

Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan, 2015, Mathematical Programming Computation on p. 149-187

Adipocyte OGT governs diet-induced hyperphagia and obesity

Min Dian Li, Nicholas B. Vera, Yunfan Yang, Bichen Zhang, Weiming Ni, Enida Ziso-Qejvanaj, Sheng Ding, Kaisi Zhang, Ruonan Yin, Simeng Wang, Xu Zhou, Ethan X. Fang, Tian Xu, Derek M. Erion, Xiaoyong Yang, 2018, Nature Communications

Testing and confidence intervals for high dimensional proportional hazards models

Ethan X. Fang, Yang Ning, Han Liu, 2017, Journal of the Royal Statistical Society. Series B: Statistical Methodology on p. 1415-1437

Accelerating Stochastic Composition Optimization

Mengdi Wang, Ji Liu, Ethan X. Fang, 2017, Journal of Machine Learning Research on p. 1-23

Accelerating stochastic composition optimization

Mengdi Wang, Ji Liu, Ethan X. Fang, 2016, Advances in Neural Information Processing Systems on p. 1722-1730

Multilevel stochastic gradient methods for nested composition optimization

Shuoguang Yang, Mengdi Wang, Ethan X. Fang, 2019, SIAM Journal on Optimization on p. 616-659

Test of significance for high-dimensional longitudinal data

Ethan X. Fang, Yang Ning, Runze Li, 2020, Annals of Statistics on p. 2622-2645

IMPLICIT BIAS OF GRADIENT DESCENT BASED ADVERSARIAL TRAINING ON SEPARABLE DATA

Yan Li, Huan Xu, Tuo Zhao, Ethan X. Fang, 2020,

Misspecified nonconvex statistical optimization for sparse phase retrieval

Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov, 2019, Mathematical Programming on p. 545-571