Discriminative Feature Feedback with General Teacher Classes

Omri Bar Oz, Tosca Lechner, Sivan Sabato

Keywords: discriminative feature feedback, interactive learning, mistake bound, teacher class

TL;DR: The theoretical proeprties of the Discriminative Feature Feedback (DFF) learning protocol under general teacher classes.

Abstract: We study the theoretical properties of the interactive learning protocol Discriminative Feature Feedback (DFF). The DFF learning protocol uses feedback in the form of discriminative feature explanations. We provide the first systematic study of DFF in a general framework comparable to that of classical protocols such as supervised learning and online learning. We study the optimal mistake bound of DFF in the realizable and non-realizable setting, and obtain novel structural results, as well as insights into the difference between Online Learning and settings with richer feedback such as DFF. We characterize the mistake bound in the realizable setting using a new notion of dimension. In the non-realizable setting, we provide a mistake upper bound and show that it cannot be improved in general. Our results show that unlike Online Learning, in DFF the the realizable dimension is insufficient to characterize the optimal non-realizable mistake bound or the existence of no-regret algorithms.

Submission Number: 53