A New Lower Bound for Agnostic Learning with Sample Compression Schemes

Steve Hanneke, Aryeh Kontorovich

Research output: Working paper/PreprintPreprint

Abstract

We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes. In particular, we find that the optimal rates of convergence for size-k agnostic sample compression schemes are of the form klog(n/k)n−−−−−−−√, which contrasts with agnostic learning with classes of VC dimension k, where the optimal rates are of the form kn−−√.
Original languageEnglish GB
StatePublished - 2018

Publication series

NameArxiv preprint

Fingerprint

Dive into the research topics of 'A New Lower Bound for Agnostic Learning with Sample Compression Schemes'. Together they form a unique fingerprint.

Cite this