2011
AISTATS
AISTATS 2011
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
Abstract
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper & Winther, 2005) with covariance decoupling techniques (Wipf & Nagarajan, 2008; Nickisch & Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Hot Topic Early Bird
— variational inference
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Keyword Pioneer
— covariance decoupling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Core Methods > Graphical Models
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Variational Inference