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Computer Science Department ColloquiumWhen and why do simpler-yet-accurate models exist? |
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Friday, March 29, 2024, 10:30am - 12:00pm |
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Speaker: Lesia Semenova
Bio
Lesia Semenova is a final-year Ph.D. candidate at Duke University in the Department of Computer Science, advised by Cynthia Rudin and Ronald Parr. Her research interests span responsible and trustworthy AI, interpretable machine learning, reinforcement learning, and AI in healthcare. She has developed a foundation for the existence of simpler-yet-accurate machine learning models. She was selected as one of the 2024 Rising Stars in Computational and Data Sciences. The student teams she has coached won the ASA Data Challenge Expo twice and placed third in a competition on scholarly document processing. Prior to joining Duke, she worked for two years at the Samsung Research and Development Institute Ukraine.
Location : CoRE 301
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Event Type: Computer Science Department Colloquium
Abstract: Finding optimal, sparse, accurate models of various forms (such as linear models with integer coefficients, rule lists, and decision trees) is generally NP-hard. Often, we do not know whether the search for a simpler model will be worthwhile, and thus we do not undertake the effort to find one. This talk addresses an important practical question: for which types of datasets would we expect interpretable models to perform as well as black-box models? I will present a mechanism of the data generation process, coupled with choices usually made by the analyst during the learning process, that leads to the existence of simpler-yet-accurate models. This mechanism indicates that such models exist in practice more often than one might expect.
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Contact Professor David Pennock
Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09
Meeting ID: 201 444 4359
Password: 550978
