The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
Abstract. An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k ...
—This paper concerns the problem of finding the minimum-length TDMA frame of a power-controlled wireless network subject to traffic demands and SINR (signalto-interference-plus...
We consider the problem of computing optimal schedules in multi-agent systems. In these problems, actions of one agent can influence the actions of other agents, while the object...
Willem Jan van Hoeve, Carla P. Gomes, Bart Selman,...
Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a minimization problem. We propose a modified Lagrangian relaxation which used i...