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She is best known for her foundational work on ranking and other new machine learning problems, as well as in applications of machine learning methods in the life sciences; for example, her recent work on predicting anticancer drug response.
At Radcliffe, Agarwal is studying computational models that can be used to understand how people make choices in the face of increasingly vast amounts of data. In particular Agarwal hopes to bring together techniques from machine learning, statistics, social choice theory, psychology, and economics to construct compact models of choice and ranking behavior that incorporate key features of human decision making. Such models could also help to shed light on how the human brain processes choice.
At Radcliffe, Agarwal is studying computational models that can be used to understand how people make choices in the face of increasingly vast amounts of data. In particular Agarwal hopes to bring together techniques from machine learning, statistics, social choice theory, psychology, and economics to construct compact models of choice and ranking behavior that incorporate key features of human decision making. Such models could also help to shed light on how the human brain processes choice.
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Papers共 70 篇Author StatisticsCo-AuthorSimilar Experts
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Shivani Agarwal,Justin Mathew,Georgia M Davis, Alethea Shephardson, Ann Levine, Rita Louard,Agustina Urrutia,Citlalli Perez-Guzman,Guillermo E Umpierrez, Limin Peng,Francisco J Pasquel
APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN ENGINEERING, SIGMA 2018, VOL 1 (2019): 533-541
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