Group-robust Machine Unlearning
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.
67d7fdac83b2126604707a84
Thomas De Min
admin_assigned
2025-03-17T14:48:36.167Z
62f79e5a92950415b637d010
/avatars/f86cd0447c827eef9b8b25f248341455.svg
Thomas De Min
user
tdemin16
67d7fdac83b2126604707a85
Subhankar Roy
67d7fdac83b2126604707a86
Stéphane Lathuilière
admin_assigned
2025-03-17T14:49:07.526Z
64b6b19811676010a443b8f9
/avatars/23320beef2c77a7d3a6736bc57530f3b.svg
Stephane Lathuilière
user
stelat
67d7fdac83b2126604707a87
Elisa Ricci
67d7fdac83b2126604707a88
Massimiliano Mancini
admin_assigned
2025-03-17T14:49:30.879Z
62cf293d3200bfd438e81f1f
/avatars/608c19ee375ef091ca77d7cfbc40e76e.svg
Massimiliano Mancini
user
massimilianom
Group-robust Machine Unlearning
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.
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.
machine unlearning forget set retain set group-robust machine unlearning sample distribution reweighting mutual information MIU group robustness mutual information calibration