Convex variational Bayesian inference for large scale generalized linear models

Nickisch, H; Seeger, MW

doi:10.1145/1553374.1553472

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Convex variational Bayesian inference for large scale generalized linear models

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Nickisch, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1553374.1553472
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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C475-0

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.