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Towards Unbounded Machine Unlearning

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arxiv 2302.09880 v3 pith:2U3ZABGJ submitted 2023-02-20 cs.LG cs.CR

Meghdad Kurmanji , Peter Triantafillou , Jamie Hayes , Eleni Triantafillou This is my paper · ORCID
classification cs.LGcs.CR
keywords unlearningmetricsapplicationsconsistentlydatadifferentforgetmachine
verification ladder T0 review T1 audit T2 compute T3 formal
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Deep machine unlearning is the problem of `removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their `right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP. At the same time, SCRUB is also consistently a top performer on metrics that measure model utility (i.e. accuracy on retained data and generalization), and is more efficient than previous work. The above are substantiated through a comprehensive empirical evaluation against previous state-of-the-art.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Space Complexity of Learning-Unlearning Algorithms

    cs.LG 2025-06 accept novelty 8.0 of 10

    The space complexity of machine unlearning for realizability testing is characterized by eluder dimension (central lower bound), star number (ticketed upper bound), and hollow star number (bounded deletions), separati...

  2. Unlearning as Distribution Restoration: A Controlled Counterfactual Study, a Validated Selective Screen, and the Limits of Oracle-Free Certification

    cs.LG 2026-07 conditional novelty 7.0 of 10

    Matching a retrained oracle on trained probes can certify models that still retain held-out forget knowledge, and oracle-free unlearning certification is only possible for counterfactual, non-inferable facts.

  3. System-Aware Unlearning Algorithms: Use Lesser, Forget Faster

    cs.LG 2025-06 conditional novelty 7.0 of 10

    The paper introduces system-aware unlearning and gives the first exact unlearning algorithm for linear classification that stores a sublinear-size core set instead of the entire dataset.

  4. Machine Unlearning for Streaming Forgetting

    cs.LG 2025-07 reject novelty 6.0 of 10

    SAFE performs streaming machine unlearning with one gradient step per deletion request using only deleted data and per-class Gaussian statistics, claiming an O(sqrt(T)+V_T) regret bound.

  5. Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble

    cs.LG 2025-06 conditional novelty 6.0 of 10

    MIAs expose different members depending on attack method and random seed; the paper quantifies this with coverage/stability and shows ensembling attacks yields stronger, more reliable privacy checks.

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