Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
In this paper, we present algorithms for Grid resource provisioning that employ agreement-based resource management. These algorithms allow userlevel resource allocation and sched...
In systems consisting of multiple clusters of processors which are interconnected by relatively slow communication links and which employ space sharing for scheduling jobs, such a...
Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applie...