| Accelerated methods for nonconvex optimization Y Carmon, JC Duchi, O Hinder, A Sidford SIAM Journal on Optimization 28 (2), 1751-1772, 2018 | 359 | 2018 |
| Lower bounds for finding stationary points I Y Carmon, JC Duchi, O Hinder, A Sidford Mathematical Programming 184 (1), 71-120, 2020 | 335 | 2020 |
| “Convex until proven guilty”: dimension-free acceleration of gradient descent on non-convex functions Y Carmon, JC Duchi, O Hinder, A Sidford International conference on machine learning, 654-663, 2017 | 173 | 2017 |
| Near-optimal methods for minimizing star-convex functions and beyond O Hinder, A Sidford, N Sohoni Conference on learning theory, 1894-1938, 2020 | 87 | 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 | 78 | 2021 |
| Lower bounds for finding stationary points II: first-order methods Y Carmon, JC Duchi, O Hinder, A Sidford Mathematical Programming 185 (1), 315-355, 2021 | 78 | 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 | 53 | 2023 |
| DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule M Ivgi, O Hinder, Y Carmon International Conference on Machine Learning, 2023 | 50 | 2023 |
| Making SGD Parameter-Free Y Carmon, O Hinder Conference on Learning Theory, 2360-2389, 2022 | 39 | 2022 |
| On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods G Haeser, O Hinder, Y Ye Mathematical Programming 186 (1), 257-288, 2021 | 23 | 2021 |
| A novel integer programing formulation for scheduling with family setup times on a single machine to minimize maximum lateness O Hinder, A Mason European Journal of Operations Research 262 (2), 411–423, 2017 | 23 | 2017 |
| A one-phase interior point method for nonconvex optimization O Hinder, Y Ye arXiv preprint arXiv:1801.03072, 2018 | 22 | 2018 |
| An efficient nonconvex reformulation of stagewise convex optimization problems R Bunel, O Hinder, S Bhojanapalli, D Krishnamurthy Advances in Neural Information Processing Systems 33, 2020 | 19 | 2020 |
| Worst-case iteration bounds for log barrier methods on problems with nonconvex constraints O Hinder, Y Ye Mathematics of Operations Research 49 (4), 2402-2424, 2024 | 13* | 2024 |
| Optimal Diagonal Preconditioning Z Qu, W Gao, O Hinder, Y Ye, Z Zhou Operations Research, 2024 | 12 | 2024 |
| Cutting plane methods can be extended into nonconvex optimization O Hinder Conference On Learning Theory, 1451-1454, 2018 | 12 | 2018 |
| Worst-case analysis of restarted primal-dual hybrid gradient on totally unimodular linear programs O Hinder Operations Research Letters 57, 107199, 2024 | 7 | 2024 |
| Datasets and benchmarks for nanophotonic structure and parametric design simulations J Kim, M Li, O Hinder, P Leu Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Multi-BOWS: multi-fidelity multi-objective Bayesian optimization with warm starts for nanophotonic structure design J Kim, M Li, Y Li, A Gómez, O Hinder, PW Leu Digital Discovery 3 (2), 381-391, 2024 | 5 | 2024 |
| The price of adaptivity in stochastic convex optimization Y Carmon, O Hinder arXiv preprint arXiv:2402.10898, 2024 | 4 | 2024 |
Yair CarmonTel Aviv UniversityBestätigte E-Mail-Adresse bei tauex.tau.ac.il
