Open Problems

Machine Unlearning

Scalability and Robustness of Machine Unlearning Under Varying Forget-Set Regimes

Effect to explainOpen
Possible candidate · 2/5 runs4 papers report this100% from 2025+

Generated automatically from the limitations stated in 4 papers (ICML, CVPR, TheWebConference), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Existing machine unlearning techniques are only validated in narrow operating windows, typically limited to small forget budgets ($\le 10\%$). As forget sets scale beyond a few hundred samples or exceed 10–20% of the training distribution, retention accuracy drops significantly (e.g., a 14–15% gap relative to retraining from scratch on CIFAR-10), while tiny splits (e.g., 1%) yield negligible unlearning. Furthermore, unlearned representations remain fragile and easily recoverable when exposed to small fractions of the forgotten data during relearning. Consequently, current unlearning algorithms cannot be reliably deployed for large-scale data deletion compliance without catastrophic utility loss.

Why it matters

Enables practitioners to deploy unlearning algorithms with predictable safety and utility bounds for arbitrary deletion request volumes, and establishes standard criteria for true irreversible forgetting beyond toy deletion splits.

Ways to approach it

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  1. 1

    Systematic Empirical Benchmark Across Forget Scales: Implement leading unlearning families (gradient ascent/reweighting, weight scrubbers, representation orthogonalization) across a unified sweep of forget fractions (0.1%, 1%, 5%, 10%, 20%, 50%) on standard benchmarks (CIFAR-100, ImageNet subsets), measuring the exact Pareto frontier between forget quality (via membership inference attacks and relearning speed) and remaining-set generalization gap against true retraining.

  2. 2

    Relearning Resilience Auditing: Measure the gradient dynamics and parameter trajectories during fine-tuning on small retention/forget mixtures (1–10% relearning budget) across various unlearning methods to quantify how deeply forgotten features are erased versus superficially masked.

  3. 3

    Subspace-Preserving Dynamic Regularization: Formulate and evaluate projection or gradient-constrained objectives that dynamically scale update penalties as the forget-set cardinality grows, measuring whether retention degradation can be bounded within 3% of retraining across $>20\%$ deletion budgets.

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Why it might fail

If theoretical lower bounds establish that approximate linear/gradient-based unlearning without full data access fundamentally degrades utility proportional to forget-set cardinality, rendering true parity with retraining impossible without storing historical training trajectories.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

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Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.