508 search results - page 13 / 102 » Learning for stochastic dynamic programming |
UAI
1998
15 years 3 months ago
1998
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
131
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ECAI
2010
Springer
15 years 3 months ago
2010
Springer
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
127
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CCE
2004
15 years 1 months ago
2004
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...
192
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COLT
2010
Springer
14 years 12 months ago
2010
Springer
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradie...
EOR
2010
15 years 2 months ago
2010
Outer linearization methods for two-stage stochastic linear programs with recourse, such as the L-shaped algorithm, generally apply a single optimality cut on the nonlinear object...