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  Convex variational Bayesian inference for large scale generalized linear models

Nickisch, H., & Seeger, M. (2009). Convex variational Bayesian inference for large scale generalized linear models. In A. Danyluk, L. Bottou, & M. Littman (Eds.), ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning (pp. 761-768). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C475-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-F923-E
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1553374.1553472 (Publisher version)
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 Creators:
Nickisch, H1, 2, Author           
Seeger, MW, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2009-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1553374.1553472
BibTex Citekey: 5864
 Degree: -

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Title: 26th International Conference on Machine Learning
Place of Event: Montreal, Canada
Start-/End Date: 2009-06-14 - 2009-06-18

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Title: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Danyluk, A, Editor
Bottou, L, Editor
Littman, M, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 761 - 768 Identifier: ISI: 978-1-60558-516-1

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Title: ACM International Conference Proceeding Series
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 382 Sequence Number: - Start / End Page: - Identifier: -