Ethan Xingyuang Fang

Assistant Professor of Statistics

Ethan Xingyuang Fang

Publication Tags

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

Most Recent Publications

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

High-dimensional Interactions Detection with Sparse Principal Hessian Matrix

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

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

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

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,

Test of significance for high-dimensional longitudinal data

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

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

Accelerating Stochastic Composition Optimization

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

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, 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,

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

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