We propose an information-theoretic approach to proving
lower bounds on the size of branching programs (b.p.). The argument
is based on Kraft-McMillan type inequalities for the average amount of
uncertainty about (or entropy of) a given input during various
stages of the computation. ...
more >>>
The worst-case complexity of an implementation of Quicksort depends
on the random number generator that is used to select the pivot
elements. In this paper we estimate the expected number of
comparisons of Quicksort as a function in the entropy of the random
source. We give upper and lower bounds ...
more >>>
We investigate the complexity of the following computational problem:
Polynomial Entropy Approximation (PEA):
Given a low-degree polynomial mapping
$p : F^n\rightarrow F^m$, where $F$ is a finite field, approximate the output entropy
$H(p(U_n))$, where $U_n$ is the uniform distribution on $F^n$ and $H$ may be any of several entropy measures.
We provide a characterization of pseudoentropy in terms of hardness of sampling: Let $(X,B)$ be jointly distributed random variables such that $B$ takes values in a polynomial-sized set. We show that $B$ is computationally indistinguishable from a random variable of higher Shannon entropy given $X$ if and only if there ... more >>>
We prove that, for all binary-input symmetric memoryless channels, polar codes enable reliable communication at rates within $\epsilon > 0$ of the Shannon capacity with a block length, construction complexity, and decoding complexity all bounded by a *polynomial* in $1/\epsilon$. Polar coding gives the *first known explicit construction* with rigorous ... more >>>
"Help bits" are some limited trusted information about an instance or instances of a computational problem that may reduce the computational complexity of solving that instance or instances. In this paper, we study the value of help bits in the settings of randomized and average-case complexity.
Amir, Beigel, and Gasarch ... more >>>
We initiate the study of the *randomness complexity* of differential privacy, i.e., how many random bits an algorithm needs in order to generate accurate differentially private releases. As a test case, we focus on the task of releasing the results of $d$ counting queries, or equivalently all one-way marginals on ... more >>>
