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ICLP
2010
Springer
13 years 11 months ago
Improving the Efficiency of Gibbs Sampling for Probabilistic Logical Models by Means of Program Specialization
Abstract. There is currently a large interest in probabilistic logical models. A popular algorithm for approximate probabilistic inference with such models is Gibbs sampling. From ...
Daan Fierens
AIPS
2008
13 years 10 months ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein
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
EMO
2006
Springer
120views Optimization» more  EMO 2006»
13 years 10 months ago
Rule Induction for Classification Using Multi-objective Genetic Programming
Multi-objective metaheuristics have previously been applied to partial classification, where the objective is to produce simple, easy to understand rules that describe subsets of a...
Alan P. Reynolds, Beatriz de la Iglesia
NIPS
2007
13 years 9 months ago
Linear programming analysis of loopy belief propagation for weighted matching
Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees. In this paper we investiga...
Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Wills...