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Ethan X. Fang
Ethan X. Fang
Associate Professor at Duke University
Verified email at duke.edu - Homepage
Title
Cited by
Cited by
Year
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
M Wang, EX Fang, H Liu
Mathematical Programming 161, 419-449, 2017
2552017
Accelerating stochastic composition optimization
M Wang, J Liu, EX Fang
Journal of Machine Learning Research 18 (105), 1-23, 2017
1482017
Generalized alternating direction method of multipliers: new theoretical insights and applications
EX Fang, B He, H Liu, X Yuan
Mathematical programming computation 7 (2), 149-187, 2015
1162015
Testing and confidence intervals for high dimensional proportional hazards model
EX Fang, Y Ning, H Liu
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018
782018
Adipocyte OGT governs diet-induced hyperphagia and obesity
MD Li, NB Vera, Y Yang, B Zhang, W Ni, E Ziso-Qejvanaj, S Ding, ...
Nature communications 9 (1), 5103, 2018
592018
Multilevel stochastic gradient methods for nested composition optimization
S Yang, M Wang, EX Fang
SIAM Journal on Optimization 29 (1), 616-659, 2019
562019
Implicit bias of gradient descent based adversarial training on separable data
Y Li, E Fang, H Xu, T Zhao
International Conference on Learning Representations, 2020
45*2020
Misspecified nonconvex statistical optimization for sparse phase retrieval
Z Yang, LF Yang, EX Fang, T Zhao, Z Wang, M Neykov
Mathematical Programming, 1-27, 2019
33*2019
Max-norm optimization for robust matrix recovery
EX Fang, H Liu, KC Toh, WX Zhou
Mathematical Programming 167, 5-35, 2018
292018
Test of significance for high-dimensional longitudinal data
EX Fang, Y Ning, R Li
Annals of statistics 48 (5), 2622, 2020
272020
Using a distributed SDP approach to solve simulated protein molecular conformation problems
X Fang, KC Toh
Distance Geometry: Theory, Methods, and Applications, 351-376, 2012
252012
Fairness-oriented learning for optimal individualized treatment rules
EX Fang, Z Wang, L Wang
Journal of the American Statistical Association 118 (543), 1733-1746, 2023
222023
Inequality in treatment benefits: Can we determine if a new treatment benefits the many or the few?
EJ Huang, EX Fang, DF Hanley, M Rosenblum
Biostatistics 18 (2), 308-324, 2017
202017
Lagrangian inference for ranking problems
Y Liu, EX Fang, J Lu
Operations research 71 (1), 202-223, 2023
162023
Nearly dimension-independent sparse linear bandit over small action spaces via best subset selection
Y Chen, Y Wang, EX Fang, Z Wang, R Li
Journal of the American Statistical Association 119 (545), 246-258, 2024
15*2024
Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming
M Rosenblum, EX Fang, H Liu
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020
152020
High-dimensional interactions detection with sparse principal hessian matrix
CY Tang, EX Fang, Y Dong
Journal of Machine Learning Research 21 (19), 1-25, 2020
152020
Offline personalized pricing with censored demand
Z Qi, J Tang, E Fang, C Shi
Offline Personalized Pricing with Censored Demand: Qi, Zhengling| uTang …, 2022
112022
Mining massive amounts of genomic data: a semiparametric topic modeling approach
EX Fang, MD Li, MI Jordan, H Liu
Journal of the American Statistical Association 112 (519), 921-932, 2017
82017
Constructing a confidence interval for the fraction who benefit from treatment, using randomized trial data
EJ Huang, EX Fang, DF Hanley, M Rosenblum
Biometrics 75 (4), 1228-1239, 2019
62019
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