Search Machine Learning Repository: Doubly Stochastic Variational Bayes for non-Conjugate Inference
Authors: Michalis Titsias and Miguel Lázaro-gredilla
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Year: 2014
Pages: 1971-1979
Abstract: We propose a simple and effective variational inference algorithm based on stochastic optimisation that can be widely applied for Bayesian non-conjugate inference in continuous parameter spaces. This algorithm is based on stochastic approximation and allows for efficient use of gradient information from the model joint density. We demonstrate these properties using illustrative examples as well as in challenging and diverse Bayesian inference problems such as variable selection in logistic regression and fully Bayesian inference over kernel hyperparameters in Gaussian process regression.
[pdf] [BibTeX]

authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).