This article proposes an algorithm to automatically learn useful transformations of data to improve accuracy in supervised classification tasks. These transformations take the for...
Current efficient planners employ an informed search guided by a heuristic function that is quite expensive to compute. Thus, ordering nodes in the search tree becomes a key issue,...
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
Iain Melvin, Jason Weston, Christina S. Leslie, Wi...
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...
Recently, Balcan and Blum [1] suggested a theory of learning based on general similarity functions, instead of positive semi-definite kernels. We study the gap between the learnin...