Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Accuracy vs. efficiency trade-offs in probabilistic diagnosis
Eighteenth national conference on Artificial intelligence
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
Systematic vs. non-systematic algorithms for solving the MPE task
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Understanding the role of noise in stochastic local search: Analysis and experiments
Artificial Intelligence
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Journal of Automated Reasoning
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Anytime AND/OR depth-first search for combinatorial optimization
AI Communications - The Symposium on Combinatorial Search
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Finding most probable explanations (MPEs) in graphical models, such as Bayesian belief networks, is a fundamental problem in reasoning under uncertainty, and much effort has been spent on developing effective algorithms for this NP-hard problem. Stochastic local search (SLS) approaches to MPE solving have previously been explored, but were found to be not competitive with state-of-the-art branch & bound methods. In this work, we identify the shortcomings of earlier SLS algorithms for the MPE problem and demonstrate how these can be overcome, leading to an SLS algorithm that substantially improves the state-of-the-art in solving hard networks with many variables, large domain sizes, high degree, and, most importantly, networks with high induced width.