Representation Unlearning: Forgetting through Information Compression

2026-01-29

AI Summary

This work proposes a novel approach to machine unlearning that efficiently and reliably fulfills privacy compliance and robustness requirements by learning a representation transformation in the model’s latent space, rather than directly modifying model parameters. Leveraging the information bottleneck principle, the method compresses information pertaining to data to be forgotten while preserving that of retained data. To the best of our knowledge, this is the first application of the information bottleneck framework to machine unlearning, enabling both zero-shot and supervised unlearning without retraining. A tractable objective function is derived via variational approximation of mutual information, allowing end-to-end optimization. Extensive experiments demonstrate that the proposed method significantly outperforms parameter-modification-based approaches across multiple benchmarks, achieving superior performance in unlearning reliability, utility preservation, and computational efficiency.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concerns. Existing approaches typically modify model parameters, but such updates can be unstable, computationally costly, and limited by local approximations. We introduce Representation Unlearning, a framework that performs unlearning directly in the model's representation space. Instead of modifying model parameters, we learn a transformation over representations that imposes an information bottleneck: maximizing mutual information with retained data while suppressing information about data to be forgotten. We derive variational surrogates that make this objective tractable and show how they can be instantiated in two practical regimes: when both retain and forget data are available, and in a zero-shot setting where only forget data can be accessed. Experiments across several benchmarks demonstrate that Representation Unlearning achieves more reliable forgetting, better utility retention, and greater computational efficiency than parameter-centric baselines.

Problem

• machine unlearning
• representation space
• information bottleneck
• privacy
• data forgetting

Innovation

• Representation Unlearning
• Information Bottleneck
• Machine Unlearning
• Zero-shot Unlearning
• Mutual Information