Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
I&C Journal 2005 Journal Article
We consider approximate PCPs for multidimensional bin-packing problems. In particular, we show how a verifier can be quickly convinced that a set of multidimensional blocks can be packed into a small number of bins. The running time of the verifier is bounded by O(log d n) where n is the number of blocks and d is the dimension.
FOCS Conference 2001 Conference Paper
Given access to independent samples of a distribution A over [n] /spl times/ [m], we show how to test whether the distributions formed by projecting A to each coordinate are independent, i. e. , whether A is /spl epsi/-close in the L/sub 1/ norm to the product distribution A/sub 1//spl times/A/sub 2/ for some distributions A/sub 1/ over [n] and A/sub 2/ over [m]. The sample complexity of our test is O/spl tilde/(n/sup 2/3/m/sup 1/3/poly(/spl epsi//sup -1/)), assuming without loss of generality that m/spl les/n. We also give a matching lower bound, up to poly (log n, /spl epsi//sup -1/) factors. Furthermore, given access to samples of a distribution X over [n], we show how to test if X is /spl epsi/-close in L/sub 1/ norm to an explicitly specified distribution Y. Our test uses O/spl tilde/(n/sup 1/2/poly(/spl epsi//sup -1/)) samples, which nearly matches the known tight bounds for the case when Y is uniform.
FOCS Conference 2000 Conference Paper
Given two distributions over an n element set, we wish to check whether these distributions are statistically close by only sampling. We give a sublinear algorithm which uses O(n/sup 2/3//spl epsiv//sup -4/ log n) independent samples from each distribution, runs in time linear in the sample size, makes no assumptions about the structure of the distributions, and distinguishes the cases when the distance between the distributions is small (less than max(/spl epsiv//sup 2//32/sup 3//spl radic/n, /spl epsiv//4/spl radic/n=)) or large (more than /spl epsiv/) in L/sub 1/-distance. We also give an /spl Omega/(n/sup 2/3//spl epsiv//sup -2/3/) lower bound. Our algorithm has applications to the problem of checking whether a given Markov process is rapidly mixing. We develop sublinear algorithms for this problem as well.