https://aaltodoc.aalto.fi/items/d40a6409-f7ed-48f7-9ede-81a59f00afb4
Gaussian process regression in atom probe tomography data reconstruction
Atom probe tomography (APT) is an imaging technique that provides precise spatial and compositional mapping of materials. APT is based on evaporating specimens...
atom probe tomographygaussian processregressiondatareconstruction
https://rdrr.io/cran/GADGET/man/gp_validate.html
gp_validate: Automated Gaussian Process Validation in GADGET: Gaussian Process Approximations for...
Automatically validates a Gaussian process (GP) using a separate validation dataset not used in the fitting of the GP. The Bastos and O'Hagan (2009) empirical...
gaussian processgpvalidateautomatedvalidation
https://gpflow.org/
GPflow - Build Gaussian process models in python
GPflow is a package for building Gaussian process models in python, using TensorFlow. It was originally created and is now managed by James Hensman and...
gaussian processbuildmodelspython
https://kth.diva-portal.org/smash/record.jsf?faces-redirect=true&language=sv&searchType=SIMPLE&query=&af=%5B%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&pid=diva2%3A1793182&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all
Antenna Array Calibration Via Gaussian Process Models
antenna arraygaussian processcalibrationviamodels
https://math.technion.ac.il/en/events/gauss-legendre-features-for-scalable-gaussian-process-regression/
Gauss-Legendre Features for Scalable Gaussian Process Regression - Faculty of Mathematics
Jan 9, 2025 - Tuesday, May 9, 2023 @ 14:30 - 15:30 - Gaussian processes provide a powerful probabilistic kernel learning framework, which allows high-quality nonparametric...
gaussian processlegendrefeaturesscalable
https://collaborate.princeton.edu/en/publications/a-gaussian-process-model-of-quasar-spectral-energy-distributions/
A Gaussian process model of quasar spectral energy distributions - Princeton University
gaussian processmodel
https://papers.neurips.cc/paper_files/paper/2013/hash/46922a0880a8f11f8f69cbb52b1396be-Abstract.html
Efficient Optimization for Sparse Gaussian Process Regression
gaussian processefficientoptimizationsparseregression
https://openreview.net/forum?id=o1woLLxcpv
Implications of Gaussian process kernel mismatch for out-of-distribution data | OpenReview
Gaussian processes provide reliable uncertainty estimates in nonlinear modeling, but a poor choice of the kernel can lead to poor generalization. Although...
gaussian processdistribution dataimplicationskernel
https://gaussianprocess.org/
Welcome to the Gaussian Process pages | the Gaussian Process web site
This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes.
welcome to thegaussian processpages website
https://www.mdpi.com/2076-3417/12/9/4789
Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models...
gaussian processslipestimationmodelplanetary
https://eprints.whiterose.ac.uk/id/eprint/151399/
Physically-inspired Gaussian process models for post-transcriptional regulation in Drosophila -...
gaussian processfor posttranscriptional regulationphysicallyinspired
https://proceedings.neurips.cc/paper_files/paper/2009/hash/92cc227532d17e56e07902b254dfad10-Abstract.html
Kernels and learning curves for Gaussian process regression on random graphs
learning curvesgaussian processkernels
https://www.isca-archive.org/interspeech_2023/wong23_interspeech.html
ISCA Archive - Distilling knowledge from Gaussian process teacher to neural network student
isca archivegaussian process
https://gpss.cc/gpss24/program
Program | Gaussian Process and Uncertainty Quantification Summer School, 2024
Gaussian Process Summer School
gaussian processuncertainty quantificationsummer schoolprogram
https://academictorrents.com/details/6d758dd0a91c0b6fd19b560b21f7af83e60f9de3/tech
A Framework for Evaluating Approximation Methods for Gaussian Process Regression - Technical -...
A Framework for Evaluating Approximation Methods for Gaussian Process Regression, Info Hash: 6d758dd0a91c0b6fd19b560b21f7af83e60f9de3
gaussian processframeworkevaluatingapproximationmethods
https://edoc.rki.de/handle/176904/13657
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
gaussian processtrustelectivelearningdistributed
https://www.catalyzex.com/paper/gaussian-process-learning-via-fisher-scoring
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation: Paper and Code. We derive a single pass algorithm for computing the gradient and...
gaussian processlearningviafisherscoring
https://portal.fis.tum.de/en/publications/prediction-with-approximated-gaussian-process-dynamical-models/
Prediction With Approximated Gaussian Process Dynamical Models - Technical University of Munich
gaussian processtechnical universitypredictionapproximated
https://www.digitado.com.br/gaussian-process-bandit-optimization-with-machine-learning-predictions-and-application-to-hypothesis-generation/
Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to...
gaussian processmachine learningbanditoptimization
https://kr.mathworks.com/help/stats/classreg.learning.regr.compactregressiongp.html
CompactRegressionGP - Compact Gaussian process regression model class - MATLAB
CompactRegressionGP is a compact Gaussian process regression (GPR) model.
gaussian processregression modelcompactclassmatlab
https://proceedings.neurips.cc/paper_files/paper/2013/hash/021bbc7ee20b71134d53e20206bd6feb-Abstract.html
Approximate Gaussian process inference for the drift function in stochastic differential equations
gaussian processfor the
https://ircommons.uwf.edu/esploro/outputs/preprint/Correcting-spatial-Gaussian-process-parameter-and/99381300845006600
Correcting spatial Gaussian process parameter and prediction variance estimation under informative...
Informative sampling designs can impact spatial prediction, or kriging, in two important ways. First, the sampling design can bias spatial covariance...
gaussian processvariance estimationcorrectingspatialparameter
https://aaltodoc.aalto.fi/items/aaa01cae-d631-4826-98a7-687939c55c89
State-Space Inference in Gaussian Process Regression Models
state spacegaussian processinferenceregressionmodels
https://infoscience.epfl.ch/entities/publication/59ced796-5979-4baf-bf40-55cb513607e9/statistics
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP)...
gaussian processbayesianmodelspac
https://www.mathworks.com/help/stats/classreg.learning.regr.compactregressiongp.html
CompactRegressionGP - Compact Gaussian process regression model class - MATLAB
CompactRegressionGP is a compact Gaussian process regression (GPR) model.
gaussian processregression modelcompactclassmatlab
https://papers.nips.cc/paper_files/paper/2008/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html
Variational Mixture of Gaussian Process Experts
gaussian processvariationalmixtureexperts
https://www.aanda.org/articles/aa/full_html/2025/07/aa54518-25/aa54518-25.html
gallifrey: JAX-based Gaussian process structure learning for astronomical time series | Astronomy &...
gaussian process
https://lore.list.lu/dataset.xhtml?persistentId=perma:LIST.0GQG9A
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling...
Sep 26, 2025 - Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on...
deep neural networkgaussian processregression
https://www.jmlr.org/beta/papers/v24/21-0556.html
Posterior Contraction for Deep Gaussian Process Priors
gaussian processposteriorcontractiondeeppriors
https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.944301/full
Frontiers | Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting
Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the...
gaussian processtime seriesfrontierssparseregression
https://gpflow.org/index.html
GPflow - Build Gaussian process models in python
GPflow is a package for building Gaussian process models in python, using TensorFlow. It was originally created and is now managed by James Hensman and...
gaussian processbuildmodelspython
https://research.utwente.nl/en/publications/hyperspectral-image-classification-using-gaussian-process-models/
Hyperspectral image classification using Gaussian process models - University of Twente Research...
university of twenteimage classificationgaussian processhyperspectralusing
https://www.osti.gov/biblio/1887418
Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science...
Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of...
gaussian processmachine learningphysics
https://papers.neurips.cc/paper_files/paper/2005/hash/3c333aadfc3ee8ecb8d77ee31197d96a-Abstract.html
Assessing Approximations for Gaussian Process Classification
gaussian processassessingapproximationsclassification
https://epjc.epj.org/articles/epjc/abs/2024/08/10052_2024_Article_13193/10052_2024_Article_13193.html
Revisiting statefinder via Gaussian process | The European Physical Journal C (EPJ C)
The European Physical Journal C (EPJ C) presents new and original research results in theoretical physics and experimental physics
european physical journal cgaussian processrevisitingvia
https://strathprints.strath.ac.uk/39447/
Direct identification of nonlinear structure using Gaussian process prior models - Strathprints
gaussian processprior modelsdirectidentificationnonlinear
https://www.secondmind.ai/research/secondmind-papers/rates-of-convergence-for-sparse-variational-gaussian-process-regression
Rates of Convergence for Sparse Variational Gaussian Process Regression | Secondmind
We prove that our Gaussian process approximations can work well with much less computational requirements than what was known before.
gaussian processratesconvergencesparsevariational
https://theses.gla.ac.uk/1367/
Implementation of gaussian process models for non-linear system identification - Enlighten Theses
implementation ofgaussian process
https://sophelio.io/use-of-a-gaussian-process-regression-in-fusion/
How Best To Use A Gaussian Process Regression In Fusion
Aug 29, 2024 - Unlock the potential of Gaussian Process Regression in fusion energy, offering advanced data analysis and insights for sustainable energy research.
to usegaussian processbestregressionfusion
https://openreview.net/forum?id=lCYrsdHb5SQ&referrer=%5Bthe%20profile%20of%20Simon%20J.%20Godsill%5D(%2Fprofile%3Fid%3D~Simon_J._Godsill1)
Non-Gaussian Process Regression | OpenReview
We extend the Gaussian process regression model to allow for locally adaptive behaviour through time-changed GPs and learn latent probabilistic representations...
gaussian processnonregressionopenreview
https://deepai.org/publication/gaussian-process-bandit-optimization-of-the-thermodynamic-variational-objective
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective | DeepAI
Oct 29, 2020 - 10/29/20 - Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the lo...
gaussian processof thebanditoptimizationthermodynamic
https://gpss.cc/
About | Gaussian Process Summer Schools
Gaussian process summer schools teach the theory and practice of Gaussian processes. This site gives details of schools past and present.
gaussian processsummerschools
https://jmlr.org/papers/v18/16-603.html
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation...
gaussian process
https://cn.comsol.com/support/learning-center/course/surrogate-modeling-theory-271/more-on-gaussian-process-surrogate-models-96171
More on Gaussian Process Surrogate Models
Get an overview of the theory behind Gaussian process regression and the radial basis function in this article.
more ongaussian processsurrogatemodels
https://asu.elsevierpure.com/en/publications/class-gp-gaussian-process-modeling-forheterogeneous-functions/
Class GP: Gaussian Process Modeling for Heterogeneous Functions - Arizona State University
gaussian processarizona stateclassgpmodeling
https://arxiv.org/abs/1406.7343
[1406.7343] Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Abstract page for arXiv paper 1406.7343: Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
gaussian process
https://uwspace.uwaterloo.ca/items/a9888f04-d457-448a-9a94-3e99c91e1504
Training Gaussian Process Regression Models Using Optimized Trajectories
Quadrotor helicopters and robot manipulators are used widely for both research and industrial applications. Both quadrotors and manipulators are difficult to...
gaussian processregression modelstrainingusingoptimized
https://deepnlp.org/equation/gaussian-process
gaussian process Equation Formula Latex Code and Summary
gaussian process , 173 users vote difficult. 41 users vote not difficult. Vote for the Top 100 Most difficult equations! Please vote for whether the equation...
gaussian processequationformulalatexcode
https://inverseprobability.com/talks/lawrence-icml07/hierarchical-span-g-span-aussian-process-latent-variable-models.html
ML@CL Hierarchical Gaussian Process Latent Variable Models
Jun 22, 2007 - Hierarchical Gaussian Process Latent Variable ModelsNeil D. LawrenceThe Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilis...
gaussian processlatent variablemlclhierarchical
https://aaltodoc.aalto.fi/items/d40a6409-f7ed-48f7-9ede-81a59f00afb4
Gaussian process regression in atom probe tomography data reconstruction
Atom probe tomography (APT) is an imaging technique that provides precise spatial and compositional mapping of materials. APT is based on evaporating specimens...
atom probe tomographygaussian processregressiondatareconstruction
https://arxiv.org/abs/1703.09710
[1703.09710] Fast and scalable Gaussian process modeling with applications to astronomical time...
Abstract page for arXiv paper 1703.09710: Fast and scalable Gaussian process modeling with applications to astronomical time series
https://rdrr.io/cran/GADGET/man/gp_validate.html
gp_validate: Automated Gaussian Process Validation in GADGET: Gaussian Process Approximations for...
Automatically validates a Gaussian process (GP) using a separate validation dataset not used in the fitting of the GP. The Bastos and O'Hagan (2009) empirical...
gaussian processgpvalidateautomatedvalidation
https://www.isca-archive.org/interspeech_2021/nakamura21_interspeech.html
ISCA Archive - Sequence-to-Sequence Learning for Deep Gaussian Process Based Speech Synthesis Using...
https://www.thetypicalset.com/sample_from_a_gaussian_process_predictive_distribution
Sample functions from a Gaussian Process
See this blog post that shows and efficient approximation.
sample functionsgaussianprocess
https://gpflow.org/
GPflow - Build Gaussian process models in python
GPflow is a package for building Gaussian process models in python, using TensorFlow. It was originally created and is now managed by James Hensman and...
gaussian processbuildmodelspython
https://pr.sdiarticle5.com/review-history/140747
Peer Review History: Modelling Spatial and Non-Linear Trends in Climate Data Using Gaussian Process...
https://kth.diva-portal.org/smash/record.jsf?faces-redirect=true&language=sv&searchType=SIMPLE&query=&af=%5B%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&pid=diva2%3A1793182&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all
Antenna Array Calibration Via Gaussian Process Models
antenna arraygaussian processcalibrationviamodels
https://iris.unibocconi.it/handle/11565/3777495
Adaptive Bayesian density estimation using Pitman-Yor or Normalized Inverse-Gaussian process kernel...
https://bytez.com/docs/arxiv/1806.11187/paper
Neural-net-induced Gaussian process regression for function approximation and PDE solution | Read...
Jun 22, 2018 - Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity of deep neural networks (deep NNs) as well as the uncertainty...