Tianyi Lin
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Welcome to my homepage! I am currently a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. My advisor is Michael I. Jordan. I am also associated with Berkeley Artificial Intelligence Research (BAIR) and RISELab. Previously, I received my B.S. in Mathematics from Nanjing University in 2011, M.S. in Pure Mathematics and Statistics from University of Cambridge in 2012 and M.S. in Operations Research from the University of California, Berkeley in 2017. From 2013 to 2015, I was a research assistant in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. My research interests include reliable and multi-agent machine learning, optimization, operations research, game theory, optimal transport and large-scale text analytics. RISELab, UC Berkeley |
Recent News
Jan. 2023: The paper First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems is accepted by Journal of Machine Learning Research.
Nov. 2022: The paper Monotone Inclusions, Acceleration and Closed-Loop Control is accepted by Mathematics of Operations Research.
Sep. 2022: The paper Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization is accepted by NeurIPS 2022.
Sep. 2022: The paper First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces is accepted by NeurIPS 2022.
May. 2022: The paper On the Efficiency of Entropic Regularized Algorithms for Optimal Transport is accepted by Journal of Machine Learning Research.
May. 2022: The paper Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback is accepted by ICML 2022.
Mar. 2022: The paper Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling is accepted by Journal of Machine Learning Research.
Feb. 2022: The paper On the Complexity of Approximating Multimarginal Optimal Transport is accepted by Journal of Machine Learning Research.