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Continuity of Gaussian Processes [PDF]
The Annals of Probability, 1986 The author first gives a generalization of \textit{M. B. Marcus} and \textit{L. A. Shepp}'s [Proc. Sixth Berkeley Sympos. math. Statist. Probab., Univ. Calif. 1970, 2, 423-441 (1972; Zbl 0379.60040)] theorem on the equivalence between sample continuity of a Gaussian process defined on a compact subset of a metric space, and a.s.
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J. Mach. Learn. Res., 2020 We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms.
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Modern Stochastics: Theory and Applications, 2015 We show that every multiparameter Gaussian process with integrable variance function admits a Wiener integral representation of Fredholm type with respect to the Brownian sheet.
Tommi Sottinen, Lauri Viitasaari
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HYPERPARAMETER OPTIMIZATION BASED ON A PRIORI AND A POSTERIORI KNOWLEDGE ABOUT CLASSIFICATION PROBLEM [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems.
Valentina S. Smirnova +3 more
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CoRR, 2021 Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous analysis of the standard maximum-likelihood objective used to train conditional NPs.
Wessel P. Bruinsma +4 more
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Gaussian processes for computer experiments
ESAIM: Proceedings and Surveys, 2017 This paper collects the contributions which were presented during the session devoted to Gaussian processes at the Journées MAS 2016. First, an introduction to Gaussian processes is provided, and some current research questions are discussed.
Bachoc François +3 more
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Design Space Exploration of Turbulent Multiphase Flows Using Machine Learning-Based Surrogate Model
Energies, 2020 This study focuses on establishing a surrogate model based on machine learning techniques to predict the time-averaged spatially distributed behaviors of vaporizing liquid jets in turbulent air crossflow for momentum flux ratios between 5 and 120.
Himakar Ganti +2 more
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Skew Gaussian processes for classification [PDF]
Machine Learning, 2020 AbstractGaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that
Alessio Benavoli +2 more
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A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis
Applied Sciences, 2020 The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it.
Cristian Torres-Valencia +4 more
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CoRR, 2012 In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We
Damianou, A.C., Lawrence, N.D.
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