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Why Forget-Only Unlearning Needs Memorization

Paper recorded by Signals 4 on 2026-10-07 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.LG · 机器学习 · first seen 2026-10-08

Abstract

Machine unlearning asks for a deletion algorithm whose output is close to retraining from scratch without the selected forget examples. In this work, we study forget-only unlearning, where the deletion algorithm receives only the trained model and the examples to forget, with no retained data or extra training information. We ask whether forget-only unlearning is always possible. We first show tha

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#333 most recent of 749 cs.LG papers we have recorded · ↑ newer: Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs · ↓ older: Oracle-Efficient and Parameter-Free Agnostic Smoothed Online Learning
Cite this page: Why Forget-Only Unlearning Needs Memorization: the #333 most recent of 749 cs.LG papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/why-forget-only-unlearning-needs-memorization.html
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