2012
NeurIPS
NeurIPS 2012
Affine Independent Variational Inference
Abstract
We present a method for approximate inference for a broad class of non-conjugate probabilistic models. In particular, for the family of generalized linear model target densities we describe a rich class of variational approximating densities which can be best fit to the target by minimizing the Kullback-Leibler divergence. Our approach is based on using the Fourier representation which we show results in efficient and scalable inference.
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Hot Topic Early Bird
- generalized linear model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Optimization
Deep Learning > Models > Variational Inference
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Information Theory
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Variational Inference