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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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Gaussian process deconvolution
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 Let us consider the deconvolution problem, i.e. to recover a latent sourcex(⋅)from the observationsy=[y1,…,yN]of a convolution processy=x⋆h+η, whereηis an additive noise, the observations inymight have missing parts with respect toy, and the filterhcould be unknown.
Felipe Tobar +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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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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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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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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Convolutional Gaussian Processes
CoRR, 2017 To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017)
van der Wilk, Mark +2 more
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Gaussian Process for Trajectories
, 2023 The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories.
Kien Nguyen 0003 +2 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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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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