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个人简介
Michael C. (Mike) Hughes works on statistical machine learning. He develops methods that find useful structure in large, messy datasets and help people make decisions in the face of uncertainty. His research interests include Bayesian hierarchical models, optimization algorithms for approximate inference, model fairness and interpretability, and applications in medicine and the sciences. Active projects include helping clinicians understand and treat diseases like depression and infertility by training probabilistic models to make personalized drug recommendations for new patients based on the thousands of electronic health records observed from previous patients.
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论文共 70 篇作者统计合作学者相似作者
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Benjamin S. Wessler,Zhe Huang, Gary M. Long,Stefano Pacifici, Nishant Prashar,Samuel Karmiy,Roman A. Sandler,Joseph Z. Sokol, Daniel B. Sokol,Monica M. Dehn,Luisa Maslon,Eileen Mai,
Proceedings of machine learning research (2023): 285-307
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arXiv (Cornell University) (2023)
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arxiv(2023)
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Machine Learning for Health Workshop (2023): 129-144
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