Remember What You Want to Forget: Algorithms for Machine Unlearning

Remember What You Want to Forget: Algorithms for Machine Unlearning (via Zoom)

Abstract: In this talk, I will talk about the problem of unlearning datapoints from a learnt model. The learner first receives a dataset S drawn i.i.d. from an unknown distribution, and outputs a model [\widehat{w}] that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint [x \in S]  can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity. For the setting of convex losses, we provide an unlearning algorithm that can unlearn up to O(n/d1/4) samples, where d is the problem dimension. In comparison, in general, differentially private learning (which implies unlearning) only guarantees deletion of O(n/d1/2) samples. This demonstrates a novel separation between differential privacy and machine unlearning.

Bio: Ayush is a PhD student in the Computer Science department at Cornell University, advised by Professor Karthik Sridharan and Professor Robert D. Kleinberg. His research interests span optimization, online learning, reinforcement learning and control, and the interplay between them. Before coming to Cornell, he spent a year at Google as a part of the Brain residency program. Before Google, he completed his undergraduate studies in computer science from IIT Kanpur in India where he was awarded the President's gold medal.