Title: Stream Native Machine Unlearning
Primary area: optimization
Keywords: machine learning, machine unlearning, convex optimization, online learning

Abstract: Machine unlearning work assumes a static, i.i.d. training environment that doesn't truly exist. Machines are expected to learn and unlearn 
We introduce the Memory Pair, the first stream-native unlearning algorithm coupling an online learning-unlearning pair. 
Our framework achieves state-of-the-art logarithmic regret bounds under strong convexity that scale naturally for nonstationary stream environments. These guarantees extend the lifespan of deployed models, reducing costly retraining while ensuring compliance with data deletion requirements. Finally, the algorithm intentionally achieves a novel sublinear memory footprint for an online machine unlearning. This makes it attractive for on-device learning, memory-constrained devices, and IoT networks where privacy is paramount.

TL;DR: We provide a novel sublinear memory footprint algorithm for online machine unlearning with the first dynamic regret analysis for online unlearning.