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ICML
2009
IEEE

Convex variational Bayesian inference for large scale generalized linear models

11 years 3 months ago
Convex variational Bayesian inference for large scale generalized linear models
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.
Hannes Nickisch, Matthias W. Seeger
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Hannes Nickisch, Matthias W. Seeger
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