title: Group-robust Machine Unlearning
summary: Machine unlearning is an emerging paradigm to remove the influence of
  specific training data (i.e., the forget set) from a model while preserving its
  knowledge of the rest of the data (i.e., the retain set). Previous approaches
  assume the forget data to be uniformly distributed from all training
  datapoints. However, if the data to unlearn is dominant in one group, we
  empirically show that performance for this group degrades, leading to fairness
  issues. This work tackles the overlooked problem of non-uniformly distributed
  forget sets, which we call group-robust machine unlearning, by presenting a
  simple, effective strategy that mitigates the performance loss in dominant
  groups via sample distribution reweighting. Moreover, we present MIU (Mutual
  Information-aware Machine Unlearning), the first approach for group robustness
  in approximate machine unlearning. MIU minimizes the mutual information between
  model features and group information, achieving unlearning while reducing
  performance degradation in the dominant group of the forget set. Additionally,
  MIU exploits sample distribution reweighting and mutual information calibration
  with the original model to preserve group robustness. We conduct experiments on
  three datasets and show that MIU outperforms standard methods, achieving
  unlearning without compromising model robustness. Source code available at
  https://github.com/tdemin16/group-robust_machine_unlearning.
paper_authors._id: 67d7fdac83b2126604707a84
paper_authors.name: Thomas De Min
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:48:36.167Z
paper_authors._id: 62f79e5a92950415b637d010
paper_authors.avatarUrl: /avatars/f86cd0447c827eef9b8b25f248341455.svg
paper_authors.fullname: Thomas De Min
paper_authors.type: user
paper_authors.user: tdemin16
paper_authors._id: 67d7fdac83b2126604707a85
paper_authors.name: Subhankar Roy
paper_authors._id: 67d7fdac83b2126604707a86
paper_authors.name: Stéphane Lathuilière
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:49:07.526Z
paper_authors._id: 64b6b19811676010a443b8f9
paper_authors.avatarUrl: /avatars/23320beef2c77a7d3a6736bc57530f3b.svg
paper_authors.fullname: Stephane Lathuilière
paper_authors.type: user
paper_authors.user: stelat
paper_authors._id: 67d7fdac83b2126604707a87
paper_authors.name: Elisa Ricci
paper_authors._id: 67d7fdac83b2126604707a88
paper_authors.name: Massimiliano Mancini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:49:30.879Z
paper_authors._id: 62cf293d3200bfd438e81f1f
paper_authors.avatarUrl: /avatars/608c19ee375ef091ca77d7cfbc40e76e.svg
paper_authors.fullname: Massimiliano Mancini
paper_authors.type: user
paper_authors.user: massimilianom
paper_title: Group-robust Machine Unlearning
paper_summary: Machine unlearning is an emerging paradigm to remove the influence of
  specific training data (i.e., the forget set) from a model while preserving its
  knowledge of the rest of the data (i.e., the retain set). Previous approaches
  assume the forget data to be uniformly distributed from all training
  datapoints. However, if the data to unlearn is dominant in one group, we
  empirically show that performance for this group degrades, leading to fairness
  issues. This work tackles the overlooked problem of non-uniformly distributed
  forget sets, which we call group-robust machine unlearning, by presenting a
  simple, effective strategy that mitigates the performance loss in dominant
  groups via sample distribution reweighting. Moreover, we present MIU (Mutual
  Information-aware Machine Unlearning), the first approach for group robustness
  in approximate machine unlearning. MIU minimizes the mutual information between
  model features and group information, achieving unlearning while reducing
  performance degradation in the dominant group of the forget set. Additionally,
  MIU exploits sample distribution reweighting and mutual information calibration
  with the original model to preserve group robustness. We conduct experiments on
  three datasets and show that MIU outperforms standard methods, achieving
  unlearning without compromising model robustness. Source code available at
  https://github.com/tdemin16/group-robust_machine_unlearning.
paper_ai_summary: MIU, an approach for group-robust machine unlearning, minimizes mutual information between model features and group information, preserving robustness and reducing performance degradation in dominant groups.
paper_ai_keywords: machine unlearning forget set retain set group-robust machine unlearning sample distribution reweighting mutual information MIU group robustness mutual information calibration