Bayesian Kullback Ying—Yang dependence reduction system and theory is presented. Via stochastic approximation, implementable algorithms and criteria are given for parameter lear...
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by...
Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a late...