We propose a novel variant of the (1 + 1)-CMA-ES that updates the distribution of mutation vectors based on both successful and unsuccessful trial steps. The computational costs o...
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an e...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials c...
This paper presents a new functional parallel language: Minimally Synchronous Parallel ML. The execution time can then be estimated and dead-locks and indeterminism are avoided. I...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials c...