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JMLR
2010
137views more  JMLR 2010»
15 years 1 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
JMLR
2010
157views more  JMLR 2010»
15 years 1 months ago
Combining Experiments to Discover Linear Cyclic Models with Latent Variables
We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relatio...
Frederick Eberhardt, Patrik O. Hoyer, Richard Sche...
JMLR
2010
172views more  JMLR 2010»
15 years 1 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
JMLR
2010
145views more  JMLR 2010»
15 years 1 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever
JMLR
2010
119views more  JMLR 2010»
15 years 1 months ago
Factorized Orthogonal Latent Spaces
Existing approaches to multi-view learning are particularly effective when the views are either independent (i.e, multi-kernel approaches) or fully dependent (i.e., shared latent ...
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, ...
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