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职业迁徙
个人简介
My research interests span the broad field of mathematical optimization and its intersection with data science, aiming to develop efficient algorithms and theories for data-driven decision making. I am particularly interested in geometric properties that can explain the outstanding performance of high-dimensional, overparameterized models and their inductive bias. My objective is to design efficient and reliable algorithms to construct practical and theoretically sound predictive models. Currently, my research focuses on four key topics:
Geometry of regularized optimization problems and induced low-complexity structures in the solutions.
Emergent phenomena in overparameterized models, especially for mixture models and feature representations.
Simple and robust methodologies for regression and causal inference.
Uncertainty quantification and calibration techniques for predictive inference.
Geometry of regularized optimization problems and induced low-complexity structures in the solutions.
Emergent phenomena in overparameterized models, especially for mixture models and feature representations.
Simple and robust methodologies for regression and causal inference.
Uncertainty quantification and calibration techniques for predictive inference.
研究兴趣
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arxiv(2023)
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NeurIPS (2023)
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arxiv(2023)
NeurIPS 2023 (2023)
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2023 57th Asilomar Conference on Signals, Systems, and Computerspp.322-329, (2023)
arxiv(2022)
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