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NIPS
1998
13 years 9 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
COMPSAC
2009
IEEE
14 years 8 days ago
A Privacy Enhancing Approach for Identity Inference Protection in Location-Based Services
—Recent advances in mobile handheld devices have facilitated the ubiquitous availability of location based services. Systems which provide location based services have always bee...
Chowdhury Sharif Hasan, Sheikh Iqbal Ahamed, Moham...
ICLP
2010
Springer
13 years 11 months ago
Abductive Inference in Probabilistic Logic Programs
ABSTRACT. Action-probabilistic logic programs (ap-programs) are a class of probabilistic logic programs that have been extensively used during the last few years for modeling behav...
Gerardo I. Simari, V. S. Subrahmanian
UAI
2001
13 years 9 months ago
Expectation Propagation for approximate Bayesian inference
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation," unifies two previous techniques: assumed-de...
Thomas P. Minka
TCS
2008
13 years 7 months ago
The computational complexity of the parallel knock-out problem
We consider computational complexity questions related to parallel knock-out schemes for graphs. In such schemes, in each round, each remaining vertex of a given graph eliminates ...
Hajo Broersma, Matthew Johnson 0002, Daniël P...