Relatively smooth convex optimization by first-order methods, and applications H Lu, RM Freund, Y Nesterov
SIAM Journal on Optimization 28 (1), 333-354, 2018
387 2018 The best of many worlds: Dual mirror descent for online allocation problems SR Balseiro, H Lu, V Mirrokni
Operations Research 71 (1), 101-119, 2023
149 * 2023 Depth creates no bad local minima H Lu, K Kawaguchi
arXiv preprint arXiv:1702.08580, 2017
125 2017 “relative continuity” for non-lipschitz nonsmooth convex optimization using stochastic (or deterministic) mirror descent H Lu
INFORMS Journal on Optimization 1 (4), 288-303, 2019
79 2019 Ordered sgd: A new stochastic optimization framework for empirical risk minimization K Kawaguchi, H Lu
International Conference on Artificial Intelligence and Statistics, 669-679, 2020
69 2020 Practical large-scale linear programming using primal-dual hybrid gradient D Applegate, M Díaz, O Hinder, H Lu, M Lubin, B O'Donoghue, W Schudy
Advances in Neural Information Processing Systems 34, 20243-20257, 2021
67 2021 Regularized online allocation problems: Fairness and beyond S Balseiro, H Lu, V Mirrokni
International Conference on Machine Learning, 630-639, 2021
49 2021 Faster first-order primal-dual methods for linear programming using restarts and sharpness D Applegate, O Hinder, H Lu, M Lubin
Mathematical Programming 201 (1), 133-184, 2023
45 2023 Accelerating gradient boosting machines H Lu, SP Karimireddy, N Ponomareva, V Mirrokni
International conference on artificial intelligence and statistics, 516-526, 2020
45 2020 The landscape of the proximal point method for nonconvex–nonconcave minimax optimization B Grimmer, H Lu, P Worah, V Mirrokni
Mathematical Programming 201 (1), 373-407, 2023
41 * 2023 Randomized gradient boosting machine H Lu, R Mazumder
SIAM Journal on Optimization 30 (4), 2780-2808, 2020
38 2020 Accelerating Greedy Coordinate Descent Methods H Lu, R Freund, V Mirrokni
International Conference on Machine Learning, 3257-3266, 2018
36 2018 Generalized stochastic frank–wolfe algorithm with stochastic “substitute” gradient for structured convex optimization H Lu, RM Freund
Mathematical Programming 187 (1), 317-349, 2021
35 2021 New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure RM Freund, H Lu
Mathematical Programming 170, 445-477, 2018
35 2018 An H Lu
Mathematical Programming 194, 1061-1112, 2022
34 * 2022 Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions S Wang, W Zhou, H Lu, A Maleki, V Mirrokni
International Conference on Machine Learning, 5228-5237, 2018
30 2018 Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming D Applegate, M Díaz, H Lu, M Lubin
SIAM Journal on Optimization 34 (1), 459-484, 2024
24 2024 Approximate leave-one-out for high-dimensional non-differentiable learning problems S Wang, W Zhou, A Maleki, H Lu, V Mirrokni
arXiv preprint arXiv:1810.02716, 2018
20 2018 On the linear convergence of extragradient methods for nonconvex–nonconcave minimax problems S Hajizadeh, H Lu, B Grimmer
INFORMS Journal on Optimization 6 (1), 19-31, 2024
9 2024 cuPDLP. jl: A GPU implementation of restarted primal-dual hybrid gradient for linear programming in Julia H Lu, J Yang
arXiv preprint arXiv:2311.12180, 2023
9 2023 
Vahab MirrokniGoogle Fellow, VP, Google ResearchGeverifieerd e-mailadres voor google.com
