| Non-asymptotic superlinear convergence of standard quasi-Newton methods Q Jin, A Mokhtari Mathematical Programming 200 (1), 425-473, 2023 | 40 | 2023 |
| Sharpened quasi-Newton methods: Faster superlinear rate and larger local convergence neighborhood Q Jin, A Koppel, K Rajawat, A Mokhtari International Conference on Machine Learning, 10228-10250, 2022 | 11 | 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, 1962-1992, 2023 | 10 | 2023 |
| Exploiting local convergence of quasi-newton methods globally: Adaptive sample size approach Q Jin, A Mokhtari Advances in Neural Information Processing Systems 34, 3824-3835, 2021 | 3 | 2021 |
| Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search Q Jin, R Jiang, A Mokhtari arXiv preprint arXiv:2404.01267, 2024 | 2 | 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, 2022 | 2 | 2022 |
| Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search Q Jin, R Jiang, A Mokhtari arXiv preprint arXiv:2404.16731, 2024 | | 2024 |
