We present a simple and scalable algorithm for maximum-margin estimation of structured output models, including an important class of Markov networks and combinatorial models. We ...
Benjamin Taskar, Simon Lacoste-Julien, Michael I. ...
Abstract--The size, heterogeneity and dynamism of the execution platforms of scientific applications, like computational grids, make using those platforms complex. Furthermore, tod...
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...
—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation,...
Lars Blackmore, Masahiro Ono, Askar Bektassov, Bri...
The distributed telecommunications sector not only requires minimal time to market, but also software that is reliable, available, maintainable and scalable. High level programmin...