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职业迁徙
个人简介
My research interests lie in machine learning theory, statistics, optimization and game theory. His research aim to develop principal and theoretical sound methodology for modern machine learning. My past research has mainly focuses on nonconvex optimization and Reinforcement Learning (RL). In nonconvex optimization, I provided the first proof showing that first-order algorithm (stochastic gradient descent) is capable of escaping saddle points efficiently. In RL, he provided the first efficient learning guarantees for Q-learning and least-squares value iteration algorithms when exploration is necessary. My works also establish the theoretical foundation for RL with function approximation, multiagent RL and partially observable RL.
研究兴趣
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ICLR 2024 (2023)
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ICLR 2023 (2023)
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MATHEMATICS OF OPERATIONS RESEARCH (2023)
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