Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Abstract. Previous works [11, 6] introduced a model of semantic communication between a “user” and a “server,” in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so. A drawback of the actual constructions is that the users are inefficient: they enumerate protocols until they discover one that is successful, leading to the potential for exponential overhead in the length of the desired protocol. Goldreich et al. [6] conjectured that this overhead could be reduced to a polynomial dependence if we restricted our attention to classes of sufficiently simple user strategies and goals. In this work, we are able to obtain such universal strategies for some reasonably general special cases by establishing an equivalence between these special cases and the usual model of mistake-bounded on-line le...

• Type: Journal
• Year: 2011
• Where: ALT
• Authors: Brendan Juba, Santosh Vempala
• Keywords: ALT 2011, Equivalence, Lower Bound, Machine Learning, Polynomial