Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions M Wang, EX Fang, H Liu Mathematical Programming 161, 419-449, 2017 | 271 | 2017 |
Accelerating stochastic composition optimization M Wang, J Liu, EX Fang Journal of Machine Learning Research 18 (105), 1-23, 2017 | 154 | 2017 |
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 | 118 | 2015 |
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 | 80 | 2018 |
Multilevel stochastic gradient methods for nested composition optimization S Yang, M Wang, EX Fang SIAM Journal on Optimization 29 (1), 616-659, 2019 | 61 | 2019 |
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 | 59 | 2018 |
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 | 49* | 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 | 34* | 2019 |
Max-norm optimization for robust matrix recovery EX Fang, H Liu, KC Toh, WX Zhou Mathematical Programming 167, 5-35, 2018 | 30 | 2018 |
Test of significance for high-dimensional longitudinal data EX Fang, Y Ning, R Li Annals of statistics 48 (5), 2622, 2020 | 29 | 2020 |
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 | 27 | 2012 |
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 | 25 | 2023 |
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 | 22 | 2017 |
Lagrangian inference for ranking problems Y Liu, EX Fang, J Lu Operations research 71 (1), 202-223, 2023 | 19 | 2023 |
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 | 18* | 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 | 16 | 2020 |
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 | 15 | 2020 |
Offline personalized pricing with censored demand Z Qi, J Tang, E Fang, C Shi Offline Personalized Pricing with Censored Demand: Qi, Zhengling| uTang …, 2022 | 12 | 2022 |
Implicit regularization of bregman proximal point algorithm and mirror descent on separable data Y Li, C Ju, EX Fang, T Zhao arXiv preprint arXiv:2108.06808, 2021 | 8 | 2021 |
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 | 8 | 2017 |