Compression in a Distributed Setting
Motivated by an attempt to understand the formation and development of (human) language, we introduce a "distributed compression" problem. In our problem a sequence of pairs of players from a set of K players are chosen and tasked to communicate messages drawn from an unknown distribution Q. Arguably languages are created and evolve to compress frequently occurring messages, and we focus on this aspect. The only knowledge that players have about the distribution Q is from previously drawn samples, but these samples differ from player to player. The only common knowledge between the players is restricted to a common prior distribution P and some constant number of bits of information (such as a learning algorithm). Letting T_eps denote the number of iterations it would take for a typical player to obtain an eps-approximation to Q in total variation distance, we ask whether T_eps iterations suffice to compress the messages down roughly to their entropy and give a partial positive answer. We show that a natural uniform algorithm can compress the communication down to an average cost per message of O(H(Q) + log (D(P || Q) + O(1)) in $\tilde{O}(T_eps)$ iterations while allowing for O(eps)-error, where D(. || .) denotes the KL-divergence more »
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10026314
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Innovations in Theoretical Computer Science (ITCS)