Machine Unlearning for Gibbs Supervised Learning Algorithms
In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired by empirical risk minimization subject to relative entropy regularization (ERM-RER). Such a method consists of maximizing the expected empirical risk over the dataset to be unlearned subject to a regularization by relative entropy with respect to the original algorithm. The optimization variable is a probability measure on the models; and the solution is another Gibbs probability measure that represents a new Gibbs supervised learning algorithm. The method guarantees exact unlearning in the sense that the new Gibbs algorithm coincides in dist...
| Authors | Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola |
|---|---|
| Primary category | stat.ML |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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