mlsanityData to 7 Oct 2026

Certified Data Removal from Machine Learning Models

arXiv 1911.03030 ↗First posted 8 Nov 2019cs.LG stat.MLcited by 512

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Cited by 409 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.

Key facts. Cited by 512 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 45; 2023: 64; 2024: 132; 2025: 135; 2026: 113.

512 papers.

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  1. 2024TOFU: A Task of Fictitious Unlearning for LLMscited by 507
  2. 2024Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearningcited by 484
  3. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  4. 2023SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generationcited by 376
  5. 2021Remember What You Want to Forget: Algorithms for Machine Unlearningcited by 360
  6. 2022A Survey of Machine Unlearningcited by 298
  7. 2023Large Language Model Unlearningcited by 288
  8. 2021Unrolling SGD: Understanding Factors Influencing Machine Unlearningcited by 274
  9. 2023Towards Unbounded Machine Unlearningcited by 261
  10. 2024MUSE: Machine Unlearning Six-Way Evaluation for Language Modelscited by 251
  11. 2024Rethinking Machine Unlearning for Large Language Modelscited by 247
  12. 2021Machine Unlearning of Features and Labelscited by 223
38 more
  1. 2023In-Context Unlearning: Language Models as Few Shot Unlearnerscited by 214
  2. 2021Fast Yet Effective Machine Unlearningcited by 209
  3. 2023Model Sparsity Can Simplify Machine Unlearningcited by 206
  4. 2022Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teachercited by 189
  5. 2023Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampeningcited by 177
  6. 2021Adaptive Machine Unlearningcited by 164
  7. 2021On the Necessity of Auditable Algorithmic Definitions for Machine Unlearningcited by 164
  8. 2023Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attackscited by 156
  9. 2024Eight Methods to Evaluate Robust Unlearning in LLMscited by 153
  10. 2022Training Data Influence Analysis and Estimation: A Surveycited by 134
  11. 2024Towards Safer Large Language Models through Machine Unlearningcited by 134
  12. 2023Boundary Unlearningcited by 132
  13. 2021Graph Unlearningcited by 129
  14. 2021Machine Unlearning via Algorithmic Stabilitycited by 128
  15. 2022Zero-Shot Machine Unlearningcited by 125
  16. 2022Continual Learning and Private Unlearningcited by 124
  17. 2022Measuring Forgetting of Memorized Training Examplescited by 118
  18. 2024Machine Unlearning of Pre-trained Large Language Modelscited by 118
  19. 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
  20. 2021Federated Unlearning via Class-Discriminative Pruningcited by 108
  21. 2024SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearningcited by 104
  22. 2023KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignmentcited by 94
  23. 2022Federated Unlearning: How to Efficiently Erase a Client in FL?cited by 90
  24. 2024Guardrail Baselines for Unlearning in LLMscited by 90
  25. 2024What makes unlearning hard and what to do about itcited by 89
  26. 2023Machine Unlearning: Solutions and Challengescited by 85
  27. 2022Deep Unlearning via Randomized Conditionally Independent Hessianscited by 83
  28. 2022Towards Adversarial Evaluations for Inexact Machine Unlearningcited by 82
  29. 2024Catastrophic Failure of LLM Unlearning via Quantizationcited by 76
  30. 2022Recommendation Unlearningcited by 71
  31. 2023Fast Model Debias with Machine Unlearningcited by 70
  32. 2023A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learningcited by 66
  33. 2023GIF: A General Graph Unlearning Strategy via Influence Functioncited by 59
  34. 2023Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutionscited by 59
  35. 2022Forget Unlearning: Towards True Data-Deletion in Machine Learningcited by 58
  36. 2024Position: LLM Unlearning Benchmarks are Weak Measures of Progresscited by 58
  37. 2022PUMA: Performance Unchanged Model Augmentation for Training Data Removalcited by 56
  38. 2024Erasing Undesirable Influence in Diffusion Modelscited by 52

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