2020
JMLR
JMLR 2020
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Abstract
This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory. [abs] [ pdf ][ bib ] © JMLR 2020. (edit, beta)
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Keyword Pioneer
— spectral gap
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Hot Topic Early Bird
— markov chain monte carlo
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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
Topics
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Stochastic Processes
Machine Learning > Optimization & Theory > Stochastic Methods
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Paradigms > Semi-Supervised Learning