Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competit...
While several powerful domain-independent planners have recently been developed, no one of these clearly outperforms all the others in every known benchmark domain. We present PbP...
Alfonso Gerevini, Alessandro Saetti, Mauro Vallati
In this paper we study, for the first time explicitly, the implications of endowing an interested party (i.e. a teacher) with the ability to modify the underlying dynamics of the ...
Zinovi Rabinovich, Lachlan Dufton, Kate Larson, Ni...
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
We develop the distance dependent Chinese restaurant process (CRP), a flexible class of distributions over partitions that allows for nonexchangeability. This class can be used to...