Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. - Qiujiang Jin; Aryan Mokhtari (NeurIPS, 2021)

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. - Qiujiang Jin; Alec Koppel; Ketan Rajawat; Aryan Mokhtari (ICML, 2022)

Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. - Ruichen Jiang; Qiujiang Jin; Aryan Mokhtari (COLT, 2023)

Non-asymptotic superlinear convergence of standard quasi-Newton methods. - Qiujiang Jin; Aryan Mokhtari (Math. Program., 2023)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. - Ruichen Jiang; Ali Kavis; Qiujiang Jin; Sujay Sanghavi; Aryan Mokhtari (NeurIPS, 2024)

Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search. - Qiujiang Jin; Ruichen Jiang; Aryan Mokhtari (NeurIPS, 2024)

Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models. - Qiujiang Jin; Tongzheng Ren; Nhat Ho; Aryan Mokhtari (Trans. Mach. Learn. Res., 2024)

Affine-Invariant Global Non-Asymptotic Convergence Analysis of BFGS under Self-Concordance. - Qiujiang Jin; Aryan Mokhtari (CoRR, 2025)