We consider the problem of identifying a planted assignment given a random $k$-SAT formula consistent with the assignment. This problem exhibits a large algorithmic gap: while the planted solution can always be identified given a formula with $O(n\log n)$ clauses, there are distributions over clauses for which the best known ... more >>>

We introduce and study the \epsilon-rank of a real matrix A, defi ned, for any  \epsilon > 0 as the minimum rank over matrices that approximate every entry of A to within an additive \epsilon. This parameter is connected to other notions of approximate rank and is motivated by ... more >>>

We develop a framework for proving lower bounds on computational problems over distributions, including optimization and unsupervised learning. Our framework is based on defining a restricted class of algorithms, called statistical algorithms, that instead of accessing samples from the input distribution can only obtain an estimate of the expectation ... more >>>

How much can randomness help computation? Motivated by this general question and by volume computation, one of the few instances where randomness provably helps, we analyze a notion of dispersion and connect it to asymptotic convex geometry. We obtain a nearly quadratic lower bound on the complexity of randomized volume ... more >>>

We prove that any real matrix $A$ contains a subset of at most
$4k/\eps + 2k \log(k+1)$ rows whose span ``contains" a matrix of
rank at most $k$ with error only $(1+\eps)$ times the error of the
best rank-$k$ approximation of $A$. This leads to an algorithm to
find such ... more >>>

We present an algorithm for learning a mixture of distributions.
The algorithm is based on spectral projection and
is efficient when the components of the mixture are logconcave
distributions.