The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...
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 approximation algorithms, integrality gaps, and hardness of approximation, of two problems related to cycles of "small" length k in a given graph. The instance f...
Abstract. We study efficient algorithms for establishing reliable connections with bandwidth guarantees in communication networks. In the normal mode of operation, each connection ...
Given an undirected graph G = (V,E) and a source vertex s ∈ V , the k-traveling repairman (KTR) problem, also known as the minimum latency problem, asks for k tours, each starti...