When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
In this paper, we present a robust feature extraction framework based on informationtheoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi's...
We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a rob...
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum
An important problem in the control of locomotion of robots with multiple degrees of freedom (e.g., biomimetic robots) is to adapt the locomotor patterns to the properties of the e...
In this paper we use policy-iteration to explore the behaviour of optimal control policies for lost sales inventory models with the constraint that not more than one replenishment...