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Convex variational Bayesian inference for large scale generalized linear models

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84109

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  MW
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Nickisch, H., & Seeger, M. (2009). Convex variational Bayesian inference for large scale generalized linear models. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), 761-768.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C475-0
Zusammenfassung
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.