Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
Abstract. We study a parametrized definition of gene clusters that permits control over the trade-off between increasing gene content versus conserving gene order within a cluster....
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
In this paper we describe the problem of Optimal Competitive Scheduling, which consists of activities that compete for a shared resource. The objective is to choose a subset of ac...
Jeremy Frank, James Crawford, Lina Khatib, Ronen I...
Abstract. Many real world problems are given in the form of multiple measurements comprising local descriptions or tasks. We propose that a dynamical organization of a population o...