2012
JMLR
JMLR 2012
A Case Study on Meta-Generalising: A Gaussian Processes Approach
Abstract
We propose a novel model for meta-generalisation, that is, performing prediction on novel tasks based on information from multiple different but related tasks. The model is based on two coupled Gaussian processes with structured covariance function; one model performs predictions by learning a constrained covariance function encapsulating the relations between the various training tasks, while the second model determines the similarity of new tasks to previously seen tasks. We demonstrate empirically on several real and synthetic data sets both the strengths of the approach and its limitations due to the distributional assumptions underpinning it. [abs] [ pdf ][ bib ] © JMLR 2012. (edit, beta)
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Meta-Learning
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Keyword Pioneer
— task similarity
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Hot Topic Early Bird
— multi-task learning
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Artificial Intelligence > Learning Paradigms > Meta-Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Learning Paradigms > Meta-Learning
Machine Learning > Bayesian & Probabilistic > Gaussian Processes