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ICML
2001
IEEE
16 years 5 months ago
Off-Policy Temporal Difference Learning with Function Approximation
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
ADHOCNOW
2004
Springer
15 years 9 months ago
Approximating the Minimum Number of Maximum Power Users in Ad Hoc Networks
Topology control is the problem of assigning transmission power values to the nodes of an ad hoc network so that the induced graph satisfies some specified property. The most fun...
Errol L. Lloyd, Rui Liu, S. S. Ravi
APPROX
2010
Springer
188views Algorithms» more  APPROX 2010»
15 years 6 months ago
Approximation Algorithms for Reliable Stochastic Combinatorial Optimization
We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F {0, 1}n . For these pro...
Evdokia Nikolova
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 3 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
ICTAI
2008
IEEE
15 years 10 months ago
The Performance of Approximating Ordinary Differential Equations by Neural Nets
—The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of compu...
Josef Fojdl, Rüdiger W. Brause