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arXiv Machine Learning By Yaiza Bermudez, Samir M. Perlaza, I\~naki Esnaola

Machine Unlearning for Gibbs Supervised Learning Algorithms

Read the original on arXiv Machine Learning →

The paper introduces a method for exact unlearning of Gibbs supervised learning algorithms via a variational formulation based on empirical risk minimization with relative entropy regularization (ERM‑RER). By maximizing the expected empirical risk over the data to be removed while regularizing with relative entropy to the original algorithm, the resulting solution is a new Gibbs probability measure that matches the distribution of an algorithm retrained from scratch on the remaining data. The approach also provides a general framework for reweighting data points in ERM‑RER, allowing for up‑ or down‑weighting to control generalization error or other objectives.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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