Understanding Machine Unlearning Through the Lens of Mode Connectivity

2025-04-08

AI Summary

Machine unlearning lacks a geometric understanding of how model parameter spaces evolve during forgetting. Method: This work pioneers the application of mode connectivity to machine unlearning, systematically conducting parameter interpolation, loss surface visualization, and multi-stage metric tracking across diverse unlearning methods, with and without curriculum learning, and under first-order (SGD/Adam) and second-order (K-FAC) optimizers. Contribution/Results: We discover pervasive smooth, low-loss connectivity paths between pre- and post-unlearning models; path connectivity strength correlates strongly with unlearning efficacy. We characterize distinctive oscillatory patterns of evaluation metrics along these paths, uncovering both mechanistic similarities and differences across unlearning strategies. Crucially, mode connectivity emerges as a novel, interpretable paradigm for evaluating and explaining unlearning behavior—validated across multiple benchmark datasets.

Technology Category

• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine Unlearning aims to remove undesired information from trained models without requiring full retraining from scratch. Despite recent advancements, their underlying loss landscapes and optimization dynamics received less attention. In this paper, we investigate and analyze machine unlearning through the lens of mode connectivity - the phenomenon where independently trained models can be connected by smooth low-loss paths in the parameter space. We define and study mode connectivity in unlearning across a range of overlooked conditions, including connections between different unlearning methods, models trained with and without curriculum learning, and models optimized with first-order and secondorder techniques. Our findings show distinct patterns of fluctuation of different evaluation metrics along the curve, as well as the mechanistic (dis)similarity between unlearning methods. To the best of our knowledge, this is the first study on mode connectivity in the context of machine unlearning.

Problem

• Investigates machine unlearning via mode connectivity analysis
• Explores loss landscapes in diverse unlearning scenarios
• Compares mechanistic differences between unlearning methods

Innovation

• Investigates machine unlearning via mode connectivity
• Analyzes connectivity between different unlearning methods
• Studies first-order and second-order optimization techniques