Behavioral Guarantees for Proxy-Based Unlearning
This paper proposes a framework generalizing recent proxy-based unlearning methods and proves theoretical guarantees about the behavior of the resulting unlearned model: upper bounds on its Kullback-Leibler divergence to the ideal posterior distribution of the retain data. We model approximate unlearning as a constrained optimization problem and interpret a family of solutions as introducing a scaled unlearning signal in the output space. The unlearning signal arises from proxies of the posterior data distributions. Its scale is adapted to the proxies to ensure the behavioral upper bounds. This framework relies on the structure of the data distributions in order to create proxies. If need...
| Authors | Virgile Dine, Teddy Furon |
|---|---|
| Primary category | cs.LG |
| Published | 11 May 2026 |
| Last updated at source | 6 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.