Qiujiang Jin
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Ph.D. student |
About me
I am a Ph.D. student at the Department of Electrical and Computer Engineering of University of Texas at Austin. I'm a member of the Wireless Networking and Communications Group. I'm advised by Prof. Aryan Mokhtari. I received my B.Sc. in pure and applied mathematics from the Department of Mathematical Sciences at Tsinghua University in 2015 and my M.Sc. in computational and mathematical engineering from the Institute for Computational & Mathematical Engineering at Stanford University in 2017. My research interests generally lie in the theory and applications of optimization. My current research focuses on the development of efficient second-order numerical optimization methods for large-scale machine learning problems.
Publications and Preprints
[Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods]
Qiujiang Jin and Aryan Mokhtari
Mathematical Programming, 1-49, 2022.[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.[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 Rates of BFGS with Exact Line Search]
Qiujiang Jin, Ruichen Jiang and Aryan Mokhtari
Under Review, 2024