![[2602.16400] Easy Data Unlearning Bench](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16400] Easy Data Unlearning Bench
Summary
The paper introduces the Easy Data Unlearning Bench, a unified benchmarking suite aimed at simplifying the evaluation of machine unlearning methods, promoting reproducibility and scalability in research.
Why It Matters
As machine learning models increasingly handle sensitive data, the ability to efficiently and effectively 'unlearn' data is crucial for compliance with privacy regulations. This benchmark addresses the technical challenges in evaluating unlearning methods, fostering best practices and accelerating research in this vital area.
Key Takeaways
- Introduces a unified benchmarking suite for machine unlearning methods.
- Simplifies evaluation processes with precomputed model ensembles and oracle outputs.
- Standardizes metrics to ensure fair comparisons across different unlearning algorithms.
- Promotes reproducibility and scalability in machine learning research.
- Code and data are publicly available, encouraging community engagement.
Computer Science > Machine Learning arXiv:2602.16400 (cs) [Submitted on 18 Feb 2026] Title:Easy Data Unlearning Bench Authors:Roy Rinberg, Pol Puigdemont, Martin Pawelczyk, Volkan Cevher View a PDF of the paper titled Easy Data Unlearning Bench, by Roy Rinberg and 3 other authors View PDF HTML (experimental) Abstract:Evaluating machine unlearning methods remains technically challenging, with recent benchmarks requiring complex setups and significant engineering overhead. We introduce a unified and extensible benchmarking suite that simplifies the evaluation of unlearning algorithms using the KLoM (KL divergence of Margins) metric. Our framework provides precomputed model ensembles, oracle outputs, and streamlined infrastructure for running evaluations out of the box. By standardizing setup and metrics, it enables reproducible, scalable, and fair comparison across unlearning methods. We aim for this benchmark to serve as a practical foundation for accelerating research and promoting best practices in machine unlearning. Our code and data are publicly available. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.16400 [cs.LG] (or arXiv:2602.16400v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.16400 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Pol Puigdemont [view email] [v1] Wed, 18 Feb 2026 12:20:32 UTC (1,121 KB) Full-text links: Access Paper: View a PDF of the paper titled Easy Data Un...
