In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach of...
Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an ...
We consider the least-square linear regression problem with regularization by the 1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptot...
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
The focus of this paper is on student learning theory. Use is made of an "analytic discovery tool" called Quantitative CyberQuest (QCQ) to help conceptualize the many go...