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Cooperative Control of Uncertain Multiagent Systems via Distributed Gaussian Processes
IEEE Transactions on Automatic Control, 2023 For single agent systems, probabilistic machine learning techniques such as Gaussian process regression have been shown to be suitable methods for inferring models of unknown nonlinearities, which can be employed to improve the performance of control ...
Armin Lederer +3 more
semanticscholar +1 more source
Water Resources Research, 2023 Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models.
Amir H. Kohanpur +6 more
doaj +1 more source
Mathematics, 2023 The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some ...
Yurong Xie, Di Wu, Zhe Qiang
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Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression [PDF]
, 2015 The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the ...
Figueiras-Vidal, AR +2 more
core +1 more source
Gaussian Process Regression on Nested Spaces
SIAM/ASA Journal on Uncertainty Quantification, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophette Blanchet-Scalliet +3 more
openaire +2 more sources
Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties
IEEE Access, 2021 Uncertainty quantification in complex engineering problems is challenging because of necessitating large numbers of expensive model evaluations. This paper proposes a two-stage framework for developing accurate machine learning-based surrogate models in ...
Farhad Pourkamali-Anaraki +1 more
doaj +1 more source
Rates of contraction of posterior distributions based on Gaussian process priors [PDF]
, 2008 We derive rates of contraction of posterior distributions on nonparametric or semiparametric models based on Gaussian processes. The rate of contraction is shown to depend on the position of the true parameter relative to the reproducing kernel Hilbert ...
van der Vaart, A. W., van Zanten, J. H.
core +6 more sources
Extrinsic Gaussian Processes for Regression and Classification on Manifolds [PDF]
Bayesian Analysis, 2017 Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although there is an increasingly vast literature on applications, methods, theory
Lizhen Lin, Mu Niu, P. Cheung, D. Dunson
semanticscholar +1 more source
Non-Gaussian Gaussian Processes for Few-Shot Regression
, 2021 Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction, and few-shot learning. GPs are particularly useful in the last application since they rely on Normal distributions and enable closed-form computation of the posterior ...
Sendera, Marcin +7 more
openaire +3 more sources
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
Journal of Statistical Software, 2022 Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.
Jamie Fairbrother +4 more
doaj +1 more source