| 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 | 305 | 2018 |
| Depth creates no bad local minima H Lu, K Kawaguchi arXiv preprint arXiv:1702.08580, 2017 | 108 | 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 | 66 | 2019 |
| The best of many worlds: Dual mirror descent for online allocation problems SR Balseiro, H Lu, V Mirrokni Operations Research, 2022 | 58 | 2022 |
| 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 | 42 | 2020 |
| Dual mirror descent for online allocation problems S Balseiro, H Lu, V Mirrokni International Conference on Machine Learning, 2020 | 39* | 2020 |
| 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 |
| Generalized stochastic Frank–Wolfe algorithm with stochastic “substitute” gradient for structured convex optimization H Lu, RM Freund Mathematical Programming 187 (1-2), 317-349, 2021 | 31 | 2021 |
| Accelerating Greedy Coordinate Descent Methods H Lu, R Freund, V Mirrokni International Conference on Machine Learning, 3257-3266, 2018 | 31 | 2018 |
| Accelerating gradient boosting machines H Lu, SP Karimireddy, N Ponomareva, V Mirrokni International Conference on Artificial Intelligence and Statistics, 516-526, 2020 | 30 | 2020 |
| Randomized gradient boosting machine H Lu, R Mazumder SIAM Journal on Optimization 30 (4), 2780-2808, 2020 | 28 | 2020 |
| Regularized online allocation problems: Fairness and beyond S Balseiro, H Lu, V Mirrokni International Conference on Machine Learning, 630-639, 2021 | 27 | 2021 |
| The landscape of the proximal point method for nonconvex–nonconcave minimax optimization B Grimmer, H Lu, P Worah, V Mirrokni Mathematical Programming, 1-35, 2022 | 24* | 2022 |
| 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 | 23 | 2021 |
| 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 | 20 | 2018 |
| An -Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to the Linear Convergence of Minimax Problems H Lu Mathematical Programming 194, 1061-1112, 2022 | 17* | 2022 |
| Faster first-order primal-dual methods for linear programming using restarts and sharpness D Applegate, O Hinder, H Lu, M Lubin Mathematical Programming, 1-52, 2022 | 14 | 2022 |
| 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 | 14 | 2018 |
| Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming D Applegate, M Díaz, H Lu, M Lubin arXiv preprint arXiv:2102.04592, 2021 | 11 | 2021 |
| Renormalized dispersion relations of -Fermi-Pasta-Ulam chains in equilibrium and nonequilibrium states WJ Shi-xiao, H Lu, D Zhou, D Cai Physical Review E 90 (3), 032925, 2014 | 8 | 2014 |
Vahab MirrokniGoogle Fellow, VP, Google ResearchVerified email at google.com
