We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approache...
We study algorithms for approximation of the mild solution of stochastic heat equations on the spatial domain ]0, 1[ d . The error of an algorithm is defined in L2-sense. We derive...
We consider linear fixed point equations and their approximations by projection on a low dimensional subspace. We derive new bounds on the approximation error of the solution, whi...
We give the first constant-factor approximation algorithm for Sparsest-Cut with general demands in bounded treewidth graphs. In contrast to previous algorithms, which rely on the f...
Eden Chlamtac, Robert Krauthgamer, Prasad Raghaven...
Given a forest F = (V, E) and a positive integer D, we consider the problem of finding a minimum number of new edges E such that in the augmented graph H = (V, E∪E ) any pair of...