Some of the currently best-known approximation algorithms for network design are based on random sampling. One of the key steps of such algorithms is connecting a set of source nod...
We study the problem of PAC-learning Boolean functions with random attribute noise under the uniform distribution. We define a noisy distance measure for function classes and sho...
Nader H. Bshouty, Jeffrey C. Jackson, Christino Ta...
Wepresent a novel, fast methodfor associationminingill high-dimensionaldatasets. OurCoincidence Detection method, which combines random sampling and Chernoff-Hoeffding bounds with...
We give polynomial time computable extractors for low-weight affince sources. A distribution is affine if it samples a random points from some unknown low dimensional subspace of ...
We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...