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UAI
2003
13 years 6 months ago
Approximate Inference and Constrained Optimization
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspo...
Tom Heskes, Kees Albers, Bert Kappen
UAI
2003
13 years 6 months ago
A Logic for Reasoning about Evidence
We introduce a logic for reasoning about evidence that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after makin...
Joseph Y. Halpern, Riccardo Pucella
UAI
2003
13 years 6 months ago
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of o...
Milos Hauskrecht, Tomás Singliar
UAI
2003
13 years 6 months ago
Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification of the reward functio...
Charles Gretton, David Price, Sylvie Thiéba...
UAI
2003
13 years 6 months ago
Locally Weighted Naive Bayes
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by thes...
Eibe Frank, Mark Hall, Bernhard Pfahringer
UAI
2003
13 years 6 months ago
Symbolic Generalization for On-line Planning
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of online p...
Zhengzhu Feng, Eric A. Hansen, Shlomo Zilberstein
UAI
2003
13 years 6 months ago
The Information Bottleneck EM Algorithm
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...
Gal Elidan, Nir Friedman