An O(log n) time, n processor randomized algorithm for computing the k-nearest neighbor graph of n points in d dimensions, for fixed d and k is presented. The method is based on t...
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
How much can an imperfect source of randomness affect an algorithm? We examine several simple questions of this type concerning the long-term behavior of a random walk on a finite...
Yossi Azar, Andrei Z. Broder, Anna R. Karlin, Nath...
Background: Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the...