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DTSTART:20210314T080000
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DTSTART;TZID=America/Chicago:20210519T123000
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DTSTAMP:20260516T075806
CREATED:20210202T202131Z
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SUMMARY:SILO: Dimitris Tsipras
DESCRIPTION:Title: Robust Machine Learning: The Worst-Case and Beyond \nAbstract:\nOne of the key challenges in the real-world deployment of machine learning models is their brittleness: their performance significantly degrades when exposed to even small variations of their training environments. \nHow can we build ML models that are more robust? \nIn this talk\, I will present a methodology for training models that are invariant to a broad family of worst-case input perturbations. I will then describe how such robust learning leads to models that learn fundamentally different data representations\, and how this can be useful even outside the adversarial context. Finally\, I will discuss model robustness beyond the worst-case: ways in which our models fail to generalize and how we can guide further progress on this front.” \nBio:\n“Dimitris Tsipras is a PhD student in the MIT EECS Department\, advised by Aleksander Mądry. His work revolves around the reliability and robustness of machine learning systems\, as well as the science of modern machine learning. He is currently being supported by a Facebook PhD Fellowship \nUNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo
URL:https://ifds.info/event/silo-05192021/
LOCATION:WI
CATEGORIES:SILO
ORGANIZER;CN="Rob%20Nowak":MAILTO:rdnowak@wisc.edu
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