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UID:338@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20251023T140000
DTEND;TZID=Europe/Paris:20251023T140000
DTSTAMP:20260828T083407Z
URL:https://isdm.umontpellier.fr/events/learning-from-unlearning-how-can-w
 e-design-ml-systems-that-are-more-trustworthy-2/
SUMMARY:Learning from unlearning : how can we design ML systems that are mo
 re trustworthy ?
DESCRIPTION:Amphi Moreau\, Building 2\, St Priest campus\n\nMachine Learnin
 g in Montpellier\, Theory &amp\; Practice - Nicolas Papernot (Univ. Toront
 o)\n\nThe talk first illustrates the challenges of having end users trust 
 that machine learning algorithms were deployed responsibly\, i.e.\, in a t
 rustworthy way\, through a deep dive on the problem of unlearning. The nee
 d for machine unlearning\, i.e.\, obtaining a model one would get without 
 training on a subset of data\, arises from privacy legislation and more re
 cently as a potential solution to data poisoning or copyright claims. As w
 e present different approaches to unlearning\, it becomes clear that they 
 fail to answer our motivating question: how can end users verify that unle
 arning was successful? Taking a step back\, we draw lessons for the broade
 r area of trustworthy machine learning and present ongoing research that l
 ay the foundations for companies\, regulators\, and countries to be able t
 o verify meaningful properties at the scale that is required for stable go
 vernance of AI algorithms\, both nationally and internationally.\n\nMachin
 e Learning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Amphi Moreau\, Building 2\, St Priest Campus\, Montpellier\, Franc
 e
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 ier\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi Moreau\, Building 2:geo:0,0
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DTSTART:20250330T030000
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