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This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric.
Theory of reproducing kernels
Nachman Aronszajn · 1950
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Solution of incorrectly formulated problems and the regularization method
Andrey Tikhonov · 1963
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Solutions of Ill-posed problems
A. N. Tikhonov and V. Y. Arsenin · 1977
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Theory of reproducing kernels and its applications
Saburou Saitoh · 1988
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Networks for approximation and learning
Tomaso Poggio and Federico Girosi · 1990
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Sparse Bayesian learning and the relevance vector machine
Michael E Tipping · 2001
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf and Alexander J Smola · 2002
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Sparse on-line gaussian processes
Lehel Csató and Manfred Opper · 2002
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Fast forward selection to speed up sparse gaussian process regression
Matthias Seeger, Christopher Williams, and Neil Lawrence · 2003
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A unifying view of sparse approximate gaussian process regression
Joaquin Quiñonero-Candela and Carl Edward Rasmussen · 2005
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Gaussian processes for machine learning
Carl Edward Rasmussen · 2006
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Local and global sparse gaussian process approximations
Edward Snelson and Zoubin Ghahramani · 2007
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Sparse probabilistic regression for activity-independent human pose inference
Raquel Urtasun and Trevor Darrell · 2008
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Mapreduce: simplified data processing on large clusters
Jeffrey Dean and Sanjay Ghemawat · 2008
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Variational learning of inducing variables in sparse gaussian processes
Michalis K Titsias · 2009
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Reproducing kernel Hilbert spaces in probability and statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
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The nature of statistical learning theory
Vladimir Vapnik · 2013
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Gaussian processes for big data
James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John William Paisley · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Distributed variational inference in sparse gaussian process regression and latent variable models
Yarin Gal, Mark van der Wilk, and Carl Edward Rasmussen · 2014
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Distributed gaussian processes
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Bayesian learning for neural networks
Radford M Neal · 2012
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Fast variational inference in the conjugate exponential family
James Hensman, Magnus Rattray, and Neil D Lawrence · 2012
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Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J Franklin, Scott Shenker, and Ion Stoica · 2012
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Marc Peter Deisenroth and Jun Wei Ng · 2015
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Variational fourier features for gaussian processes
James Hensman, Nicolas Durrande, and Arno Solin · 2016
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String and membrane gaussian processes
Yves-Laurent Kom Samo and Stephen J Roberts · 2016
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Scalable transformed additive signal decomposition by non-conjugate gaussian process inference
Vincent Adam, James Hensman, and Maneesh Sahani · 2016
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Mllib: Machine learning in apache spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, et al · 2016
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