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个人简介
Jamieson’s research explores how to leverage already-collected data to inform what future measurements to make next, in a closed loop. Such active learning can extract considerably richer insights than any measurement plan fixed in advance, using the same statistical budget. His work ranges from theory to practical algorithms with guarantees to open-source machine learning systems and has been adopted in a range of applications, including measuring human perception in psychology studies, adaptive A/B/n testing in dynamic web-environments, numerical optimization, and choosing hyperparameters for deep neural networks.
研究兴趣
论文共 102 篇作者统计合作学者相似作者
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CoRR (2024)
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Jifan Zhang,Lalit Jain,Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh,Andrew Wagenmaker,Scott Sievert,Timothy Rogers,Kevin Jamieson,Robert Mankoff,Robert Nowak
arxiv(2024)
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International Conference on Artificial Intelligence and Statisticspp.1585-1593, (2024)
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International Conference on Artificial Intelligence and Statistics (2023): 1684-1692
International Conference on Artificial Intelligence and Statistics (2023): 2602-2610
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