Alternate Learning and Compression Approaching R(D)

Abstract

The inherent trade-off in on-line learning is between exploration and exploitation. A good balance between these two (conflicting) goals can achieve a better long-term performance. Can we define an optimal balance? We propose to study this question through a backward-adaptive lossy compression system, which exhibits a "natural" trade-off between exploration and exploitation.

This paper was presented as a poster in the workshop `Learn 2 Compress', in ISIT 2024, Athens, Greece, July 2024. It was processed and reviewed in the Open Review system