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NeurIPS 2023 Machine Unlearning Competition

Eleni Triantafillou · Fabian Pedregosa · Meghdad Kurmanji · Kairan ZHAO · Gintare Karolina Dziugaite · Peter Triantafillou · Ioannis Mitliagkas · Vincent Dumoulin · Lisheng Sun · Peter Kairouz · Julio C Jacques Junior · Jun Wan · Sergio Escalera · Isabelle Guyon

Room 355


We are proposing the first competition on machine unlearning, to our knowledge. 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. This is particularly challenging in the context of deep models, which tend to memorize information from their training data, thus compromising privacy. The lack of a standardized evaluation protocol has hindered the development of unlearning, which is a relatively new area of research. Our challenge is designed to fill this need. 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. We propose a realistic scenario for unlearning face images.

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