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ICML
2010
IEEE
13 years 9 months ago
Learning Efficiently with Approximate Inference via Dual Losses
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...
ICML
2001
IEEE
14 years 8 months ago
Continuous-Time Hierarchical Reinforcement Learning
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...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ESCIENCE
2006
IEEE
14 years 2 months ago
Application-Level Resource Provisioning on the Grid
In this paper, we present algorithms for Grid resource provisioning that employ agreement-based resource management. These algorithms allow userlevel resource allocation and sched...
Gurmeet Singh, Carl Kesselman, Ewa Deelman
CCGRID
2003
IEEE
14 years 1 months ago
The Performance of Processor Co-Allocation in Multicluster Systems
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...
Anca I. D. Bucur, Dick H. J. Epema
WSC
2008
13 years 10 months ago
Approximate dynamic programming: Lessons from the field
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...
Warren B. Powell