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DTSTART;TZID=America/Los_Angeles:20241107T160000
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DTSTAMP:20260404T033654
CREATED:20250828T200431Z
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UID:7292-1730995200-1730998800@tilos.ai
SUMMARY:TILOS Seminar: Data Models for Deep Learning: Beyond i.i.d. Assumptions
DESCRIPTION:Elchanan Mossel\, Professor of Mathematics\, MIT \nAbstract: Classical Machine Learning theory is largely built upon the assumption that data samples are independent and identically distributed (i.i.d.) from general distribution families. In this talk\, I will present novel insights that emerge when we move beyond these traditional assumptions\, exploring both dependent sampling scenarios and structured generative distributions. These perspectives offer fresh theoretical frameworks and practical implications for modern machine learning approaches. \n\nElchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT)\, specializing in probability theory\, combinatorics\, and theoretical computer science. His research explores a range of complex\, interdisciplinary problems\, including social choice theory\, inference in networks\, and the analysis of algorithms\, with applications across economics\, political science\, and genetics. Mossel completed his Ph.D. at the Hebrew University of Jerusalem and held postdoctoral positions at Microsoft Research and UC Berkeley before joining MIT. Recognized for his innovative work\, Mossel has received a Sloan fellowship\, NSF CAREER award\, and COLT best paper award\, and is a Fellow of the American Mathematical Society.
URL:https://tilos.ai/event/tilos-seminar-data-models-for-deep-learning-beyond-i-i-d-assumptions/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/mossel-elchanan-e1728935276435-milFYz.jpg
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