In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for dir...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
Future agent applications will increasingly represent human users autonomously or semi-autonomously in strategic interactions with similar entities. Hence, there is a growing need...
Dynamic power management schemes (also called policies) can be used to control the power consumption levels of electronic systems, by setting their components in different states,...
Giuseppe A. Paleologo, Luca Benini, Alessandro Bog...
Positioning in learning networks is a process that assists learners in finding a starting point and an efficient route in the network that will foster competence building. In orde...
Jan van Bruggen, Ellen Rusman, Bas Giesbers, Rob K...