We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
The target of machine learning is a predictive model that performs well on unseen data. Often, such a model has multiple intended uses, related to different points in the tradeoff ...
Alan P. Reynolds, David W. Corne, Michael J. Chant...
We introduce Fortuna, the first tool for model checking priced probabilistic timed automata (PPTAs). Fortuna can handle the combination of real-time, probabilistic and cost feature...
Jasper Berendsen, David N. Jansen, Frits W. Vaandr...