The problem of automatic feature selection/weighting in kernel methods is examined. We work on a formulation that optimizes both the weights of features and the parameters of the ...
Many real world applications employ multivariate performance measures and each example can belong to multiple classes. The currently most popular approaches train an SVM for each ...
In this paper, we consider two variance reduction schemes that exploit the structure of the primal graph of the graphical model: Rao-Blackwellised w-cutset sampling and AND/OR sam...
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In th...
Current exact algorithms for score-based structure discovery in Bayesian networks on n nodes run in time and space within a polynomial factor of 2n . For practical use, the space ...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
Round-based games are an instance of discrete planning problems. Some of the best contemporary game tree search algorithms use random roll-outs as data. Relying on a good policy, ...
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
This paper focuses on the use of nuclear DNA Short Tandem Repeat traits for the identification of the victims of a Mass Fatality Incident. The goal of the analysis is the assessme...