Mo' Memory, Mo' Problems: Stream-Native Machine Unlearning

2025-08-13

AI Summary

Existing machine unlearning research is confined to static, i.i.d. batch settings, rendering it ill-suited for stream-driven continual learning and real-time prediction. Method: This work pioneers the integration of machine unlearning into online learning, proposing the first theoretically grounded unlearning framework for streaming environments. It establishes a unified analytical framework centered on regret, sample complexity, and deletion capacity; tightens the cumulative regret upper bound to $O(ln T)$; and designs an efficient online L-BFGS–based update mechanism that avoids Hessian inversion, ensuring constant memory overhead and instantaneous model deletion. Contribution/Results: The method significantly reduces retraining frequency while preserving model accuracy, offering a provably correct, low-overhead, and scalable unlearning paradigm tailored for production-grade continual learning systems.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Machine unlearning work assumes a static, i.i.d training environment that doesn't truly exist. Modern ML pipelines need to learn, unlearn, and predict continuously on production streams of data. We translate the notion of the batch unlearning scenario to the online setting using notions of regret, sample complexity, and deletion capacity. We further tighten regret bounds to a logarithmic $mathcal{O}(ln{T})$, a first for a machine unlearning algorithm. And we swap out an expensive Hessian inversion with online variant of L-BFGS optimization, removing a memory footprint that scales linearly with time. Such changes extend the lifespan of an ML model before expensive retraining, making for a more efficient unlearning process.

Problem

• Addresses static assumptions in machine unlearning
• Enables continuous learning and unlearning on data streams
• Reduces memory footprint with efficient optimization

Innovation

• Online machine unlearning with regret bounds
• Logarithmic regret bound O(ln T)
• Online L-BFGS replaces Hessian inversion