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Gaussian Processes for Machine Learning
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TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.Abstract:
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.read more
Citations
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Journal ArticleDOI
An informative path planning framework for UAV-based terrain monitoring
Marija Popovic,Teresa Vidal-Calleja,Gregory Hitz,Jen Jen Chung,Inkyu Sa,Roland Siegwart,Juan Nieto +6 more
TL;DR: This article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori.
Posted Content
Meta Reinforcement Learning with Latent Variable Gaussian Processes
TL;DR: This paper frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data and shows that this results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines.
Journal ArticleDOI
Stochastic model updating utilizing Bayesian approach and Gaussian process model
TL;DR: Bayesian approach is adopted in SMU for parameter uncertainty quantification, and the use of the powerful variance-based global sensitivity analysis (GSA) in parameter selection to exclude non-influential parameters from calibration parameters, which yields a reduced-order model and thus further alleviates the computational burden.
Journal ArticleDOI
Reconstructing Gravity on Cosmological Scales
TL;DR: In this article, a data-driven late-time reconstruction of gravity theories and dark energy models on cosmological scales is presented, which exploits the power of the effective field theory approach to dark energy and modified gravity phenomenology.
Journal ArticleDOI
A sea-level database for the Pacific coast of central North America
Simon E. Engelhart,Matteo Vacchi,Benjamin P. Horton,Benjamin P. Horton,Alan R. Nelson,Robert E. Kopp +5 more
TL;DR: A database of published and new relative sea level (RSL) data for the past 16 ka constrains the sea-level histories of the Pacific coast of central North America (southern British Columbia to central California) as discussed by the authors.
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TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.