mlsanityData to 7 Oct 2026

Making AI Forget You: Data Deletion in Machine Learning

arXiv 1907.05012 ↗First posted 11 Jul 2019cs.LG stat.MLcited by 447

Not on the map. Why?

Cited by 364 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an example of this effort. In this paper we initiate a framework studying what to do when it is no longer permissible to deploy models derivative from specific user data. In particular, we formulate the problem of efficiently deleting individual data points from trained machine learning models. For many standard ML models, the only way to completely remove an individual's data is to retrain the whole model from scratch on the remaining data, which is often not computationally practical. We investigate algorithmic principles that enable efficient data deletion in ML. For the specific setting of k-means clustering, we propose two provably efficient deletion algorithms which achieve an average of over 100X improvement in deletion efficiency across 6 datasets, while producing clusters of comparable statistical quality to a canonical k-means++ baseline.

Key facts. Cited by 447 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 52; 2023: 66; 2024: 110; 2025: 110; 2026: 85.

447 papers.

Sort:

By area:

  1. 2024Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearningcited by 484
  2. 2022Knowledge Unlearning for Mitigating Privacy Risks in Language Modelscited by 424
  3. 2023SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generationcited by 376
  4. 2023Detecting Pretraining Data from Large Language Modelscited by 375
  5. 2021Remember What You Want to Forget: Algorithms for Machine Unlearningcited by 360
  6. 2023Ablating Concepts in Text-to-Image Diffusion Modelscited by 312
  7. 2022A Survey of Machine Unlearningcited by 298
  8. 2023Towards Unbounded Machine Unlearningcited by 261
  9. 2024MUSE: Machine Unlearning Six-Way Evaluation for Language Modelscited by 251
  10. 2024Rethinking Machine Unlearning for Large Language Modelscited by 247
  11. 2021Machine Unlearning of Features and Labelscited by 223
  12. 2023In-Context Unlearning: Language Models as Few Shot Unlearnerscited by 214
38 more
  1. 2021Fast Yet Effective Machine Unlearningcited by 209
  2. 2023Model Sparsity Can Simplify Machine Unlearningcited by 206
  3. 2023To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Nowcited by 204
  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. 2024Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Modelscited by 164
  9. 2024Towards Safer Large Language Models through Machine Unlearningcited by 134
  10. 2023Boundary Unlearningcited by 132
  11. 2021Graph Unlearningcited by 129
  12. 2021Machine Unlearning via Algorithmic Stabilitycited by 128
  13. 2022Zero-Shot Machine Unlearningcited by 125
  14. 2022Continual Learning and Private Unlearningcited by 124
  15. 2022Measuring Forgetting of Memorized Training Examplescited by 118
  16. 2024Machine Unlearning of Pre-trained Large Language Modelscited by 118
  17. 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
  18. 2021Federated Unlearning via Class-Discriminative Pruningcited by 108
  19. 2024SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearningcited by 104
  20. 2023KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignmentcited by 94
  21. 2022Federated Unlearning: How to Efficiently Erase a Client in FL?cited by 90
  22. 2024Guardrail Baselines for Unlearning in LLMscited by 90
  23. 2024What makes unlearning hard and what to do about itcited by 89
  24. 2023Machine Unlearning: Solutions and Challengescited by 85
  25. 2024Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Differencecited by 85
  26. 2022Deep Unlearning via Randomized Conditionally Independent Hessianscited by 83
  27. 2022Towards Adversarial Evaluations for Inexact Machine Unlearningcited by 82
  28. 2024Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearningcited by 81
  29. 2024Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacycited by 78
  30. 2024Catastrophic Failure of LLM Unlearning via Quantizationcited by 76
  31. 2025Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyondcited by 76
  32. 2022Recommendation Unlearningcited by 71
  33. 2022Federated Unlearning with Knowledge Distillationcited by 70
  34. 2023Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifierscited by 66
  35. 2023A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learningcited by 66
  36. 2023GIF: A General Graph Unlearning Strategy via Influence Functioncited by 59
  37. 2022Forget Unlearning: Towards True Data-Deletion in Machine Learningcited by 58
  38. 2024Position: LLM Unlearning Benchmarks are Weak Measures of Progresscited by 58

50 most cited of 447 All 447 (JSON)