| Non-asymptotic superlinear convergence of standard quasi-Newton methods Q Jin, A Mokhtari Mathematical Programming 200 (1), 425-473, 2023 | 42 | 2023 |
| Sharpened quasi-Newton methods: Faster superlinear rate and larger local convergence neighborhood Q Jin, A Koppel, K Rajawat, A Mokhtari The Thirty-Ninth International Conference on Machine Learning (ICML 2022 …, 2022 | 13 | 2022 |
| Online learning guided curvature approximation: A quasi-Newton method with global non-asymptotic superlinear convergence R Jiang, Q Jin, A Mokhtari The Thirty-Sixth Annual Conference on Learning Theory (COLT 2023), 1962-1992, 2023 | 12 | 2023 |
| Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search Q Jin, R Jiang, A Mokhtari arXiv preprint arXiv:2404.01267, 2024 | 4 | 2024 |
| Statistical and computational complexities of BFGS quasi-Newton method for generalized linear models Q Jin, T Ren, N Ho, A Mokhtari Transactions on Machine Learning Research 2024 (TMLR 2024), 2022 | 3 | 2022 |
| Exploiting local convergence of quasi-newton methods globally: Adaptive sample size approach Q Jin, A Mokhtari The Thirty-Fifth Annual Conference on Neural Information Processing Systems …, 2021 | 3 | 2021 |
| Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization R Jiang, A Kavis, Q Jin, S Sanghavi, A Mokhtari The Thirty-Eighth Annual Conference on Neural Information Processing Systems …, 2024 | 1 | 2024 |
| Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search Q Jin, R Jiang, A Mokhtari The Thirty-Eighth Annual Conference on Neural Information Processing Systems …, 2024 | | 2024 |
| Non-asymptotic study of quasi-Newton methods with application in large-scale machine learning Q Jin | | 2024 |
