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JAIR
1998
198views more  JAIR 1998»
14 years 9 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
ALT
2010
Springer
14 years 11 months ago
Inferring Social Networks from Outbreaks
We consider the problem of inferring the most likely social network given connectivity constraints imposed by observations of outbreaks within the network. Given a set of vertices ...
Dana Angluin, James Aspnes, Lev Reyzin
TSP
2011
230views more  TSP 2011»
14 years 4 months ago
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
JMLR
2012
13 years 3 days ago
Deterministic Annealing for Semi-Supervised Structured Output Learning
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on unlabeled data to deal with the combinatorial nature ...
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Be...
INFOCOM
2009
IEEE
15 years 4 months ago
Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference
—A novel formulation for optimal sensor selection and in-network fusion for distributed inference known as the prizecollecting data fusion (PCDF) is proposed in terms of optimal ...
Animashree Anandkumar, Meng Wang, Lang Tong, Anant...