Theory of Computing
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Title : Simplified Separation of Information and Communication
Authors : Anup Rao and Makrand Sinha
Volume : 14
Number : 20
Pages : 1-29
URL : https://theoryofcomputing.org/articles/v014a020
Abstract
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We give an example of a boolean function whose information complexity
is exponentially smaller than its communication complexity. Such a
separation was first demonstrated by Ganor, Kol and Raz (J. ACM 2016).
We give a simpler proof of the same result. In the course of this
simplification, we make several new contributions: we introduce a new
communication lower-bound technique, the notion of a _fooling
distribution_, which allows us to separate information and
communication complexity, and we also give a more direct proof of the
information complexity upper bound.
We also prove a generalization of Shearer's Lemma that may be of
independent interest. A version of Shearer's original lemma bounds the
expected mutual information of a subset of random variables with
another random variable, when the subset is chosen independently of
all the random variables that are involved. Our generalization allows
some dependence between the random subset and the random variables
involved, and still gives us similar bounds with an appropriate error
term.
A preliminary version of this paper appeared in ECCC as Technical Report TR15-057.