We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned b...
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Pe...
This work presents a lookahead-based exploration strategy for a model-based learning agent that enables exploration of the opponent's behavior during interaction in a multi-a...
In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level assoc...
Abstract. Empirical hardness models are a recent approach for studying NP-hard problems. They predict the runtime of an instance using efficiently computable features. Previous res...
Dealing with interference is one of the primary challenges to solve in the design of protocols for wireless ad-hoc networks. Most of the work in the literature assumes localized o...