![]() ![]() We propose a realistic scenario for unlearning face images. By incentivizing the development of better unlearning algorithms, informing the community of their relative strengths and weaknesses, and unifying evaluation criteria, we expect our competition to have a significant impact. Our challenge is designed to fill this need. The lack of a standardized evaluation protocol has hindered the development of unlearning, which is a relatively new area of research. ![]() This is particularly challenging in the context of deep models, which tend to memorize information from their training data, thus compromising privacy. Unlearning is a rapidly growing area of research that has emerged in response to one of the most significant challenges in deep learning: allowing users to exercise their right to be forgotten. Jacques Junior (University of Barcelona), Jun Wan (NLPR, CASIA), Sergio Escalera (CVC and University of Barcelona), Isabelle Guyon (CNRS, INRIA, University Paris-Saclay, ChaLearn, Google DeepMind)Ĭontact: are proposing the first competition on machine unlearning, to our knowledge. NeurIPS 2023 Machine Unlearning CompetitionĮleni Triantafillou (Google DeepMind), Fabian Pedregosa (Google DeepMind), Meghdad Kurmanji (University of Warwick), Kairan Zhao (University of Warwick), Gintare Karolina Dziugaite (Google DeepMind), Peter Triantafillou (University of Warwick), Ioannis Mitliagkas (Mila, University of Montreal, Google DeepMind), Vincent Dumoulin (Google DeepMind), Lisheng Sun (LRI, Université Paris Saclay), Peter Kairouz (Google Research), Julio C. Please note that all information is subject to change, visit the competition websites regularly and contact the organizers of each competition directly for more information. Below you will find a brief summary of accepted competitions NeurIPS 2023.Ĭompetitions are grouped by category, all prizes are tentative and depend solely on the organizing team of each competition and the corresponding sponsors. ![]()
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