基本信息
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职业迁徙
个人简介
I am interested in enabling scientific discovery by extending statistical machine learning methods. In recent years, we have developed new optimisation methods for solving problems such as ranking, feature selection and experimental design, with the aim of solving scientific questions in collaboration with experts in other fields. This has included diverse problems in genomics, systems biology, and astronomy. The long term goal is to use active learning, bandits, and choice theory for design of experiments. I am co-author of the textbook Mathematics for Machine Learning. I support and advocate open source software and reproducible research in the context of machine learning, for example via a JMLR special track, as well as the community sites mloss.org and mldata.org.
研究兴趣
论文共 140 篇作者统计合作学者相似作者
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Methods in molecular biology (Clifton, N.J.) (2024): 319-344
arXiv (Cornell University) (2023)
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Machine Learningno. 8 (2023): 2787-2789
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CoRRno. 2 (2023): 3116-3129
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NeurIPS (2023)
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ICLR 2023 (2022)
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