Computer Science/Discrete Mathematics Seminar I
How to Sketch a Learning Algorithm
How does the choice of training data influence an AI model? This question is of central importance to interpretability, privacy, and basic science. At its core is the data deletion problem: after a reasonable amount of precomputation, quickly predict how the model would behave if a given subset of training data had been deleted.
I will present a data deletion scheme capable of predicting model outputs with arbitrarily small error ε and failure probability δ in the deep learning setting. The precomputation and prediction algorithms are only Õ(log(1/δ)/ε^2) factors slower than regular training and inference, respectively. The storage requirements are those of Õ(log(1/δ)/ε^2) models.
The proof is based on a simple assumption about the stability of training. In contrast to the assumptions made by prior work, stability appears to be fully compatible with learning powerful AI models. At a technical level, our work is based on a new method for locally sketching an arithmetic circuit by computing higher-order derivatives in random complex directions.
Based on my recent work: https://arxiv.org/pdf/2604.07328.