2015
AISTATS
AISTATS 2015
Scalable Variational Gaussian Process Classification
Abstract
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, out-performing the state of the art on benchmark datasets. Importantly, the variational formulation an be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Keyword Pioneer
— variational inducing point
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Hot Topic Early Bird
— stochastic optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Models > Variational Inference
Machine Learning > Learning Types > Classification
Machine Learning > Bayesian & Probabilistic > Variational Inference
Machine Learning > Bayesian & Probabilistic > Gaussian Processes