State-of-the-art research on automated learning of ontologies from text currently focuses on inexpressive ontologies. The acquisition of complex axioms involving logical connective...
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
In this paper, we present an improved version of the online phase-space learning algorithm of Tsung and Cottrell (1995), called ARTISTE (Autonomous Real-TIme Selection of Training...
Imitation learning, also called learning by watching or programming by demonstration, has emerged as a means of accelerating many reinforcement learning tasks. Previous work has s...
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...