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职业迁徙
个人简介
Wang's research interests lie in the broad area of statistical machine learning — a research field that addresses the statistical and computational properties of machine learning algorithms and their optimality guarantees. Specifically, his work focuses on developing provable and practical methods for various challenging learning regimes (e.g., high dimensional, heterogeneous, privacy-constrained, sequential, parallel and distributed) and often involves exploiting hidden structures in data (generalized sparsity, union-of-subspace, graph or network structures), balancing various resources (model complexity, statistical power and privacy budgets) as well as developing scalable optimization tools (e.g., those tailored for deep learning).
研究兴趣
论文共 149 篇作者统计合作学者相似作者
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arxiv(2024)
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CoRR (2024)
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IEEE Journal on Selected Areas in Information Theoryno. 99 (2024): 1-1
arxiv(2024)
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CSCWDpp.825-830, (2023)
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Estee Y. Cramer,Evan L. Ray,Velma K. Lopez,Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding,Tilmann Gneiting,Katie H. House,Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji,
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICAno. 15 (2023)
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