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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461801.0, MPI für biologische Kybernetik / Biologische Kybernetik
Convex variational Bayesian inference for large scale generalized linear models
Authors:Nickisch, H.; Seeger, M.W.
Editors:Danyluk, A.; Bottou, L.; Littman, M.
Date of Publication (YYYY-MM-DD):2009-06
Title of Proceedings:Proceedings of the 26th International Conference on Machine Learning (ICML 2009)
Start Page:761
End Page:768
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. We provide a generic double loop algorithm for solving this relaxation on models with arbitrary super-Gaussian potentials. By iteratively decoupling the criterion, most of the work can be done by solving large linear systems, rendering our algorithm orders of magnitude faster than previously proposed solvers for the same problem. We evaluate our method on problems of Bayesian active learning for large binary classification models, and show how to address settings with many candidates and sequential inclusion steps.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:5864
URL:http://www.cs.mcgill.ca/~icml2009/
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