In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class speciļ...
This paper describes a new learning by example mechanism and its application for digital circuit design automation. This mechanism uses finite state machines to represent the infer...
We consider the problem of learning to follow a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts...
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
We consider the problem of online learning in settings in which we want to compete not simply with the rewards of the best expert or stock, but with the best trade-oļ¬ between rew...
Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman