We consider the policy search approach to reinforcement learning. We show that if a “baseline distribution” is given (indicating roughly how often we expect a good policy to v...
J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff...
We consider the task of suggesting related queries to users after they issue their initial query to a web search engine. We propose a machine learning approach to learn the probab...
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
Modeling the cognitive processes of learners is fundamental to build educational software that are autonomous and that can provide highly tailored assistance during learning [3]. F...
Philippe Fournier-Viger, Roger Nkambou, Andr&eacut...
This paper proposes a unique map learning method for mobile robots based on the co-visibility infor mation of objects i.e., the information on whether two objects are visible at...