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UID:206@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250612T140000
DTEND;TZID=Europe/Paris:20250612T140000
DTSTAMP:20260825T132319Z
URL:https://isdm.umontpellier.fr/events/when-to-forget-complexity-trade-of
 fs-in-machine-unlearning/
SUMMARY:When to Forget? Complexity Trade-offs in Machine Unlearning
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Martin Van Waerebeke (Inria)\n\
 nMachine Unlearning (MU) aims at removing the influence of specific data p
 oints from a trained model\, striving to achieve this at a fraction of the
  cost of full model retraining. In this paper\, we analyze the efficiency 
 of unlearning methods and establish the first upper and lower bounds on mi
 nimax computation times for this problem\, characterizing the performance 
 of the most efficient algorithm against the most difficult objective funct
 ion. Specifically\, for strongly convex objective functions and under the 
 assumption that the forget data is inaccessible to the unlearning method\,
  we provide a phase diagram for the unlearning complexity ratio---a novel 
 metric that compares the computational cost of the best unlearning method 
 to full model retraining. The phase diagram reveals three distinct regimes
 : one where unlearning at a reduced cost is infeasible\, another where unl
 earning is trivial because adding noise suffices\, and a third where unlea
 rning achieves significant computational advantages over retraining. These
  findings highlight the critical role of factors such as data dimensionali
 ty\, the number of samples to forget\, and privacy constraints in determin
 ing the practical feasibility of unlearning.\n\nMachine Learning in Montpe
 llier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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