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个人简介
Professor Angluin is interested in machine learning and computational learning theory. Algorithmic modeling and analysis of learning tasks gives insight into the phenomena of learning, and suggests avenues for the creation of tools to help people learn, and for the design of “smarter” software and artificial agents that flexibly adapt their behavior. Professor Angluin’s thesis was among the first work to apply complexity theory to the field of inductive inference. Her work on learning from positive data reversed a previous dismissal of that topic, and established a flourishing line of research. Her work on learning with queries established the models and the foundational results for learning with membership queries. Recently, her work has focused on the areas of coping with errors in the answers to queries, map-learning by mobile robots, and fundamental questions in modeling the interaction of a teacher and a learner.
研究兴趣
论文共 139 篇作者统计合作学者相似作者
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arxiv(2024)
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ICGI (2023)
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CoRR (2023)
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European Joint Conferences on Theory And Practice of Software (2022): 325-343
arXiv (Cornell University) (2022)
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