Ethan Xingyuang Fang
Assistant Professor of Statistics
-
421B Thomas
University Park, PA - xxf13@psu.edu
- 814-865-3235
Links
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
Effectiveness and cost-effectiveness of an integrated digital psychological intervention (EmoEase) in Chinese chronic obstructive pulmonary disease patients: Study protocol of a randomized controlled trial
Simiao Chen, Wenjin Chen, Yanfei Li, Yiwen Yu, Qiushi Chen, Lirui Jiao, Ke Huang, Xunliang Tong, Pascal Geldsetzer, Aditi Bunker, Xingyuan Fang, Shu Jing, Yuhao Liu, Yanming Li, Liu He, Chao Wang, Weiyu Wang, Zhoutao Zheng, Shiyu Zhang, Jinghan Zhao, Ting Yang, Till Bärnighausen, Chen Wang, 2024, Digital Health
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
Fairness-Oriented Learning for Optimal Individualized Treatment Rules
Ethan X. Fang, Zhaoran Wang, Lan Wang, 2023, Journal of the American Statistical Association on p. 1733-1746
Lagrangian Inference for Ranking Problems
Yue Liu, Ethan X. Fang, Junwei Lu, 2023, Operations Research on p. 202-223
Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games
Zihan Zhu, Ethan X. Fang, Zhuoran Yang, 2023, Advances in Neural Information Processing Systems on p. 47902-47913
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
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao, 2023, 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
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
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, 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,
Max-norm optimization for robust matrix recovery
Ethan X. Fang, Han Liu, Kim Chuan Toh, Wen Xin Zhou, 2018, Mathematical Programming on p. 5-35