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个人简介
I'm generally interested in mathematical foundations of machine learning, e.g., statistical learning theory and deep learning theory of under-/over-parameterized models. Currently I'm also interested in reinforcement learning theory, especially on function approximation, from classical statistical learning to sequential decision making.
My research interest starts from kernel methods to large-scale computational methodologies in algorithm, and mainly focuses on theoretically understanding generalization properties of machine learning based algorithms, especially on over-parameterized models (motivated by neural networks). In fact, my research line can be understood from a function space theory perspective, from RKHS to hyper-RKHS, Barron spaces, Besov spaces. This aims to understand the role of over-parameterization from kernel methods to neural networks.
My research interest starts from kernel methods to large-scale computational methodologies in algorithm, and mainly focuses on theoretically understanding generalization properties of machine learning based algorithms, especially on over-parameterized models (motivated by neural networks). In fact, my research line can be understood from a function space theory perspective, from RKHS to hyper-RKHS, Barron spaces, Besov spaces. This aims to understand the role of over-parameterization from kernel methods to neural networks.
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ICLR 2024 (2024)
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AAAI 2024no. 20 (2024): 22674-22674
ICLR 2024 (2024)
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ICLR 2024 (2024)
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CoRR (2024)
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CoRR (2024)
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