# Remember What You Want to Forget: Algorithms for Machine Unlearning arXiv: https://arxiv.org/abs/2103.03279 · HTML: https://mlsanity.com/paper/2103.03279/ ## Key facts - Cited by 371 papers in the map (data through 14 Oct 2026). - Placed in Machine unlearning, one of 12 directions in LLM safety and security. - Builds on 19 of its 39 references that are in the map. - First posted on arXiv 4 Mar 2021. ## Placed in - LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/) ## Builds on (most cited first, 19 of 19) - [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016) - [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019) - [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018) - [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019) - [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014) - [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019) - [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019) - [Descent-to-Delete: Gradient-Based Methods for Machine Unlearning](https://mlsanity.com/paper/2007.02923/) (2020) - [Approximate Data Deletion from Machine Learning Models](https://mlsanity.com/paper/2002.10077/) (2020) - [Robust Estimators in High Dimensions without the Computational Intractability](https://mlsanity.com/paper/1604.06443/) (2016) - [When Machine Unlearning Jeopardizes Privacy](https://mlsanity.com/paper/2005.02205/) (2020) - [Private Stochastic Convex Optimization with Optimal Rates](https://mlsanity.com/paper/1908.09970/) (2019) - [Mixed-Privacy Forgetting in Deep Networks](https://mlsanity.com/paper/2012.13431/) (2020) - [Agnostic Estimation of Mean and Covariance](https://mlsanity.com/paper/1604.06968/) (2016) - [Machine Unlearning via Algorithmic Stability](https://mlsanity.com/paper/2102.13179/) (2021) - [Variational Bayesian Unlearning](https://mlsanity.com/paper/2010.12883/) (2020) - [Formalizing Data Deletion in the Context of the Right to be Forgotten](https://mlsanity.com/paper/2002.10635/) (2020) - [Towards Probabilistic Verification of Machine Unlearning](https://mlsanity.com/paper/2003.04247/) (2020) - [Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back](https://mlsanity.com/paper/1608.04414/) (2016) ## Built on by (most cited first, 30 of 371) - [Erasing Concepts from Diffusion Models](https://mlsanity.com/paper/2303.07345/) (2023) - [TOFU: A Task of Fictitious Unlearning for LLMs](https://mlsanity.com/paper/2401.06121/) (2024) - [Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning](https://mlsanity.com/paper/2404.05868/) (2024) - [Detecting Pretraining Data from Large Language Models](https://mlsanity.com/paper/2310.16789/) (2023) - [SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation](https://mlsanity.com/paper/2310.12508/) (2023) - [Ablating Concepts in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.13516/) (2023) - [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022) - [Unrolling SGD: Understanding Factors Influencing Machine Unlearning](https://mlsanity.com/paper/2109.13398/) (2021) - [Towards Unbounded Machine Unlearning](https://mlsanity.com/paper/2302.09880/) (2023) - [MUSE: Machine Unlearning Six-Way Evaluation for Language Models](https://mlsanity.com/paper/2407.06460/) (2024) - [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024) - [In-Context Unlearning: Language Models as Few Shot Unlearners](https://mlsanity.com/paper/2310.07579/) (2023) - [Fast Yet Effective Machine Unlearning](https://mlsanity.com/paper/2111.08947/) (2021) - [Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://mlsanity.com/paper/2310.20150/) (2023) - [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023) - [Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher](https://mlsanity.com/paper/2205.08096/) (2022) - [Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening](https://mlsanity.com/paper/2308.07707/) (2023) - [Adaptive Machine Unlearning](https://mlsanity.com/paper/2106.04378/) (2021) - [On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning](https://mlsanity.com/paper/2110.11891/) (2021) - [Towards Safer Large Language Models through Machine Unlearning](https://mlsanity.com/paper/2402.10058/) (2024) - [Boundary Unlearning](https://mlsanity.com/paper/2303.11570/) (2023) - [Zero-Shot Machine Unlearning](https://mlsanity.com/paper/2201.05629/) (2022) - [Machine Unlearning of Pre-trained Large Language Models](https://mlsanity.com/paper/2402.15159/) (2024) - [Measuring Forgetting of Memorized Training Examples](https://mlsanity.com/paper/2207.00099/) (2022) - [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022) - [SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://mlsanity.com/paper/2404.18239/) (2024) - [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022) - [Machine Unlearning: Solutions and Challenges](https://mlsanity.com/paper/2308.07061/) (2023) - [Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference](https://mlsanity.com/paper/2406.08607/) (2024) - [Deep Unlearning via Randomized Conditionally Independent Hessians](https://mlsanity.com/paper/2204.07655/) (2022) ## Abstract (arXiv) We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset S drawn i.i.d. from an unknown distribution, and outputs a model widehatw that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint z ∈ S can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity. For the setting of convex losses, we provide an unlearning algorithm that can unlearn up to O(n/d1/4) samples, where d is the problem dimension. In comparison, in general, differentially private learning (which implies unlearning) only guarantees deletion of O(n/d1/2) samples. This demonstrates a novel separation between differential privacy and machine unlearning.