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Bagging for Gaussian process regression
Neurocomputing, 2009This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data are re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions.
Tao Chen 0009, Jianghong Ren
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Bounded Gaussian process regression
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework.
Bjørn Sand Jensen +2 more
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Recursive Gaussian process regression
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013For large data sets, performing Gaussian process regression is computationally demanding or even intractable. If data can be processed sequentially, the recursive regression method proposed in this paper allows incorporating new data with constant computation time.
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Divisive Gaussian Processes for Nonstationary Regression
IEEE Transactions on Neural Networks and Learning Systems, 2014Standard Gaussian process regression (GPR) assumes constant noise power throughout the input space and stationarity when combined with the squared exponential covariance function. This can be unrealistic and too restrictive for many real-world problems.
Luis Munoz-Gonzalez +2 more
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Gaussian process regression with skewed errors
Journal of Computational and Applied Mathematics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. T. Alodat, Mohammed K. Shakhatreh
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Distributed robust Gaussian Process regression
Knowledge and Information Systems, 2017We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-t likelihood. The posterior distribution is approximated by a Laplace Approximation, and together with concepts from Bayesian Committee Machines, we efficiently distribute the computations and ...
Sebastian Mair 0001, Ulf Brefeld
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Efficient sparsification for Gaussian process regression
Neurocomputing, 2016Abstract Sparse Gaussian process models provide an efficient way to perform regression on large data sets. Sparsification approaches deal with the selection of a representative subset of available training data for inducing the sparse model approximation.
Jens Schreiter +2 more
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Gaussian Process Regression with Fluid Hyperpriors
2004A Gaussian process model can be learned from data by identifying the covariance matrix of its sample values. The matrix usually depends on some fixed parameters called input length scales. Their estimation is equivalent to finding the corresponding diffeomorphism of the process inputs.
Ramunas Girdziusas, Jorma Laaksonen
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Asynchronous Distributed Gaussian Process Regression
Proceedings of the AAAI Conference on Artificial IntelligenceIn this paper, we address a practical distributed Bayesian learning problem with asynchronous measurements and predictions due to diverse computational conditions. To this end, asynchronous distributed Gaussian process (AsyncDGP) regression is proposed, which is the first effective online distributed Gaussian processes (GPs) approach to improve the ...
Zewen Yang, Xiaobing Dai, Sandra Hirche
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Model Selection for Gaussian Process Regression
2017Gaussian processes are powerful tools since they can model non-linear dependencies between inputs, while remaining analytically tractable. A Gaussian process is characterized by a mean function and a covariance function (kernel), which are determined by a model selection criterion.
Nico S. Gorbach +4 more
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