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Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification.
Learning gaussian processes from multiple tasks
Kai Yu, Volker Tresp, and Anton Schwaighofer · 2005
Earlier work this paper cites.
Spatial modelling using a new class of nonstationary covariance functions
Christopher J Paciorek and Mark J Schervish · 2006
Earlier work this paper cites.
Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
Earlier work this paper cites.
Kernels and designs for modelling invariant functions: From group invariance to additivity
David Ginsbourger, Nicolas Durrande, and Olivier Roustant · 2013
Earlier work this paper cites.
A survey on multi-output regression
Hanen Borchani, Gherardo Varando, Concha Bielza, and Pedro Larrañaga · 2015
Earlier work this paper cites.
Regression-based covariance functions for nonstationary spatial modeling
Mark D Risser and Catherine A Calder · 2015
Cited alongside, same era.
Estimating shape constrained functions using gaussian processes
Xiaojing Wang and James O Berger · 2016
Cited alongside, same era.
Hybrid genetic deflated newton method for global optimisation
Marcus M Noack and Simon W Funke · 2017
Cited alongside, same era.
Constrained gaussian process learning for model predictive control
Janine Matschek, Andreas Himmel, Kai Sundmacher, and Rolf Findeisen · 2019
Cited alongside, same era.
Polarized inelastic neutron scattering of nonreciprocal spin waves in mnsi
Tobias Weber, Johannes Waizner, Paul Steffens, Andreas Bauer, Christian Pfleiderer, Markus Garst, and Peter Böni · 2019
Cited alongside, same era.
Advances in kriging-based autonomous x-ray scattering experiments
Marcus M Noack, Gregory S Doerk, Ruipeng Li, Masafumi Fukuto, and Kevin G Yager
Cited in the paper.
Autonomous materials discovery driven by gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
Marcus M Noack et al
Cited in the paper.
A review of kernel methods for feature extraction in nonlinear process monitoring
Karl Ezra Pilario, Mahmood Shafiee, Yi Cao, Liyun Lao, and Shuang-Hua Yang · 2020
Later among the works it cites.
A survey of constrained gaussian process regression: Approaches and implementation challenges
Laura Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, and John Jakeman · 2020
Later among the works it cites.
A framework for interdomain and multioutput gaussian processes
Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, and James Hensman · 2020
Later among the works it cites.
Autonomous data acquisition for large scale facilities
Marcus M et al. Noack · 2021
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