Qiujiang Jin
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Ph.D. student |
About me
I am a Ph.D. student from the Department of Electrical and Computer Engineering of University of Texas at Austin under the advice of Aryan Mokhtari. My research interests generally lie in the theory and applications of continuous optimization algorithms. My current research focuses on the design and analysis of numerical optimization methods for large-scale machine learning problems.
Publications and Preprints
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach
Qiujiang Jin and Aryan Mokhtari
Advances in Neural Information Processing Systems (NeurIPS), 2021.Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods
Qiujiang Jin and Aryan Mokhtari
Mathematical Programming, 1-49, 2022.Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Qiujiang Jin, Alec Koppel, Ketan Rajawat and Aryan Mokhtari
International Conference on Machine Learning (ICML), 2022.Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence
Ruichen Jiang, Qiujiang Jin and Aryan Mokhtari
Conference on Learning Theory (COLT), 2023.Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models
Qiujiang Jin, Tongzheng Ren, Nhat Ho and Aryan Mokhtari
Transactions on Machine Learning Research, 2024.Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search
Qiujiang Jin, Ruichen Jiang and Aryan Mokhtari
Advances in Neural Information Processing Systems (NeurIPS), 2024. (Spotlight)Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi and Aryan Mokhtari
Advances in Neural Information Processing Systems (NeurIPS), 2024.Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search
Qiujiang Jin, Ruichen Jiang and Aryan Mokhtari
Under Review, 2024