We consider the problem of estimating the average of a huge set of values.
That is,
given oracle access to an arbitrary function $f:\{0,1\}^n\mapsto[0,1]$,
we need to estimate $2^{-n} \sum_{x\in\{0,1\}^n} f(x)$
upto an additive error of $\epsilon$.
We are allowed to employ a randomized algorithm which may ... more >>>

A Uniform Generation procedure for $NP$ is an
algorithm which given any input in a fixed NP-language, outputs a uniformly
distributed NP-witness for membership of the input in the language.
We present a Uniform Generation procedure for $NP$ that runs in probabilistic
polynomial-time with an NP-oracle. This improves upon ... more >>>

We introduce "resource-bounded betting games", and propose
a generalization of Lutz's resource-bounded measure in which the choice
of next string to bet on is fully adaptive. Lutz's martingales are
equivalent to betting games constrained to bet on strings in lexicographic
order. We show that if strong pseudo-random number generators exist,
more >>>

We present a weaker variant of the PCP Theorem that admits a
significantly easier proof. In this
variant the prover only has $n^t$ time to compute each
bit of his answer, for an arbitray but fixed constant
$t$, in contrast to
being all powerful. We show that
3SAT ... more >>>

We present a novel technique, based on the Jensen-Shannon divergence
from information theory, to prove lower bounds on the query complexity
of sampling algorithms that approximate functions over arbitrary
domain and range. Unlike previous methods, our technique does not
use a reduction from a binary decision problem, but rather ... more >>>

We initiate a general study of the randomness complexity of
property testing, aimed at reducing the randomness complexity of
testers without (significantly) increasing their query complexity.
One concrete motovation for this study is provided by the
observation that the product of the randomness and query complexity
of a tester determine ... more >>>

We give new evidence that quantum computers -- moreover, rudimentary quantum computers built entirely out of linear-optical elements -- cannot be efficiently simulated by classical computers. In particular, we define a
model of computation in which identical photons are generated, sent through a linear-optical network, then nonadaptively measured to count ... more >>>

We obtain the first deterministic extractors for sources generated (or sampled) by small circuits of bounded depth. Our main results are:

(1) We extract $k (k/nd)^{O(1)}$ bits with exponentially small error from $n$-bit sources of min-entropy $k$ that are generated by functions $f : \{0,1\}^\ell \to \{0,1\}^n$ where each output ... more >>>

There has been considerable interest lately in the complexity of distributions. Recently, Lovett and Viola (CCC 2011) showed that the statistical distance between a uniform distribution over a good code, and any distribution which can be efficiently sampled by a small bounded-depth AC0 circuit, is inverse-polynomially close to one. That ... more >>>

We obtain the first deterministic randomness extractors
for $n$-bit sources with min-entropy $\ge n^{1-\alpha}$
generated (or sampled) by single-tape Turing machines
running in time $n^{2-16 \alpha}$, for all sufficiently
small $\alpha > 0$. We also show that such machines
cannot sample a uniform $n$-bit input to the Inner
Product function ... more >>>

This note proves the existence of a quadratic GF(2) map
$p : \{0,1\}^n \to \{0,1\}$ such that no constant-depth circuit
of size $\poly(n)$ can sample the distribution $(u,p(u))$
for uniform $u$.

A map $f:[n]^{\ell}\to[n]^{n}$ has locality $d$ if each output symbol
in $[n]=\{1,2,\ldots,n\}$ depends only on $d$ of the $\ell$ input
symbols in $[n]$. We show that the output distribution of a $d$-local
map has statistical distance at least $1-2\cdot\exp(-n/\log^{c^{d}}n)$
from a uniform permutation of $[n]$. This seems to be the ... more >>>

Given a distribution over $[n]^n$ such that any $k$ coordinates need $k/\log^{O(1)}n$ bits of communication to sample, we prove that any map that samples this distribution from uniform cells requires locality $\Omega(\log(n/k)/\log\log(n/k))$. In particular, we show that for any constant $\delta>0$, there exists $\varepsilon=2^{-\Omega(n^{1-\delta})}$ such that $\Omega(\log n/\log\log n)$ non-adaptive ... more >>>

Spurred by the influential work of Viola (Journal of Computing 2012), the past decade has witnessed an active line of research into the complexity of (approximately) sampling distributions, in contrast to the traditional focus on the complexity of computing functions.

We build upon and make explicit earlier implicit results of ... more >>>