Ethan Xingyuang Fang

Assistant Professor of Statistics

Ethan Xingyuang Fang

Publication Tags

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

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

Lagrangian Inference for Ranking Problems

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

Robust matrix estimations meet Frank–Wolfe algorithm

Naimin Jing, Ethan X. Fang, Cheng Yong Tang, 2023, Machine Learning

Fairness-Oriented Learning for Optimal Individualized Treatment Rules

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

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

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,

Test of significance for high-dimensional longitudinal data

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

High-dimensional interactions detection with sparse principal hessian matrix

Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong, 2020, Journal of Machine Learning Research

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

Using a distributed SDP approach to solve simulated protein molecular conformation problems

Xingyuan Fang, Kim Chuan Toh, 2013, on p. 351-376

Inequality in treatment benefits: Can we determine if a new treatment benefits the many or the few?

Emily J. Huang, Ethan X. Fang, Daniel F. Hanley, Michael Rosenblum, 2017, Biostatistics on p. 308-324

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