# Random Relabeling for Efficient Machine Unlearning

arXiv: https://arxiv.org/abs/2305.12320 · HTML: https://mlsanity.com/paper/2305.12320/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 21 May 2023.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [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)
- [Machine Unlearning: Linear Filtration for Logit-based Classifiers](https://mlsanity.com/paper/2002.02730/) (2020)

## Built on by (most cited first, 7 of 7)

- [Corrective Machine Unlearning](https://mlsanity.com/paper/2402.14015/) (2024)
- [Multi-Objective Large Language Model Unlearning](https://mlsanity.com/paper/2412.20412/) (2024)
- [Verifying Machine Unlearning with Explainable AI](https://mlsanity.com/paper/2411.13332/) (2024)
- [Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation](https://mlsanity.com/paper/2410.23693/) (2024)
- [Class Unlearning via Depth-Aware Removal of Forget-Specific Directions](https://mlsanity.com/paper/2604.15166/) (2026)
- [Delete and Retain: Efficient Unlearning for Document Classification](https://mlsanity.com/paper/2512.13711/) (2025)
- [Multi-Objective Reference-Aligned Machine Unlearning](https://mlsanity.com/paper/2606.00399/) (2026)

## Abstract (arXiv)

Learning algorithms and data are the driving forces for machine learning to bring about tremendous transformation of industrial intelligence. However, individuals' right to retract their personal data and relevant data privacy regulations pose great challenges to machine learning: how to design an efficient mechanism to support certified data removals. Removal of previously seen data known as machine unlearning is challenging as these data points were implicitly memorized in training process of learning algorithms. Retraining remaining data from scratch straightforwardly serves such deletion requests, however, this naive method is not often computationally feasible. We propose the unlearning scheme random relabeling, which is applicable to generic supervised learning algorithms, to efficiently deal with sequential data removal requests in the online setting. A less constraining removal certification method based on probability distribution similarity with naive unlearning is further developed for logit-based classifiers.