Gaussian Processes

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Overview

Gaussian processes model unknown functions by using kernels to describe how values at different inputs relate, producing both predictions and uncertainty estimates. Research develops kernels for nonstationary, multi-output, deep, and structured data, alongside scalable inference methods such as inducing points, Vecchia approximations, Kronecker algebra, and matrix-free computation. These methods support applications including Bayesian optimization, spatial and time-series forecasting, robotics, scientific simulation, and uncertainty-aware classification. A central tension is how to scale inference and adapt kernels to complex data while retaining accurate, well-calibrated uncertainty and reliable decisions.

Latest papers

CardsList

115 papers24 papers in the last four weeks, up 71% on the four weeks before. 0.1% of all new papers.

  1. Probabilistic Meshless Methods on Manifolds

    Oct 29, 2026Federico Pavesi, Noémie Jaquier, Antonio CandelieriPDE SolvingPDE Surrogate Modeling

    Department of Economics, Management and Statistics University of Milano-Bicocca Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy · Department of Robotics, Perception and Learning KTH Royal Institute of Technology Lindstedtsvägen 24, 10044 Stockholm, Sweden

  2. A Gaussian Process State Space Formulation for Materials Constitutive Modeling: Application to Quasi-brittle Materials

    Oct 23, 2026Houlin Xu, Chenyang Li, K. T. Ramesh +1State Space ModelsMaterials Science

    Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA · Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA

  3. R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes

    Oct 19, 2026Javier Enrique Aguilar, Soham Mukherjee, David KohnsGaussian ProcessesBayesian Inference

    TU Dortmund, RC TRUST (UA Ruhr), Germany · Aalto University, Finland

  4. Structured Relevance Determination and Channel Additive Gaussian Processes for Image Data

    Oct 18, 2026Harald Roßels, Ludovico ScartonGaussian ProcessesKernel Methods

    Independent Researcher · Bonn-Rhein-Sieg University of Applied Sciences

  5. Structure-Aware Spatial Kernels for Gaussian Process Distributed Port-Hamiltonian Systems

    Oct 15, 2026Tianyi Yang, Thomas BeckersKernel MethodsGaussian Processes

    Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA · Institute for Measurement, Control and Microtechnology, Ulm University, 89081 Ulm, Germany

  6. Learn Only Where You Can't Compute: Structured Corrections to Gaussian Process Posteriors

    Oct 15, 2026Tobias Weber, Carla Sagebiel, Philipp HennigGaussian ProcessesPhysics-Informed Generative Modeling

    Tübingen AI Center, Tübingen, Germany

  7. Time-partitioning Gaussian Process Mixture Model

    Oct 14, 2026Guilherme Piantino, Hedibert F. LopesTime Series ModelingNon-Stationary Time Series

    Insper Institute of Education and Research, São Paulo

  8. Convergence Rates of Gaussian Process Thompson Sampling for Efficient Global Optimization

    Oct 14, 2026Haowei Wang, Yilin ZhengThompson SamplingGlobal Optimization

    National University of Singapore Singapore

  9. Bayesian Optimization of CVaR with Unknown Hyperparameters

    Oct 13, 2026Valentin Margraf, Jonas Hanselle, Viktor Bengs +3Bayesian OptimizationConditional Value-at-Risk

    LMU Munich · Munich Center for Machine Learning (MCML) · German Research Center for Artificial Intelligence (DFKI) +3

  10. Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

    Oct 13, 2026Luis Wiederhold, Nil Ayday, Debarghya GhoshdastidarConvergence AnalysisNeural Network Approximation Theory

    School of Computation, Information and Technology Technical University of Munich

  11. GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction

    Oct 12, 2026Andre MarchildonGaussian ProcessesGaussian Process Regression

    Applied Scientist at Amazon Prime Air, Seattle, USA

  12. Understanding Hidden Spatial Preferences in Bayesian Optimization

    Oct 10, 2026Maria Bånkestad, Jens SjölundBayesian OptimizationUncertainty-Aware Exploration

  13. Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints

    Oct 9, 2026Boyuan Deng, Kshitiz Upadhyay, Michael ShieldsPDE SolvingGaussian Processes

    Department of Civil and Systems Engineering, Johns Hopkins University · Department of Aerospace Engineering and Mechanics, University of Minnesota

  14. ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach

    Oct 8, 2026Kaichuang Yang, Håvard Rue, Jakob ZeitlerBayesian OptimizationBayesian Inference

    KAUST · University of Oxford

  15. Ghost tasking for parametrized Gaussian Processes solving linear differential equations

    Oct 8, 2026Johanna Moser, Christopher Albert, Sascha RanftlGaussian ProcessesPhysics-Informed ML

  16. Feature Space Adaptation for Effortless Gaussian Process Flows

    Oct 8, 2026Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck +1Maximum Marginal Likelihood EstimationGaussian Processes

    University of Cambridge · University College London · University of New South Wales +1

  17. Derivative Gaussian Processes on a Two-Direction Budget

    Oct 7, 2026Hyunseok Seung, Matthias KatzfussGaussian ProcessesGaussian Process Regression

    University of Wisconsin–Madison

  18. What Can a Gaussian Process Design Test

    Oct 7, 2026Ivan De Boi, Marnix Van SoomGaussian ProcessesStatistical Hypothesis Testing

    Department of Electromechanical Engineering University of Antwerp Campus Groenenborger – Z373 Groenenborgerlaan 171, 2020 Antwerp, Belgium · Artificial Intelligence Lab Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels, Belgium

  19. Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel

    Oct 7, 2026Ameer Qaqish, Sudipto Banerjee, Didong LiGaussian ProcessesKernel Methods

    Department of Biostatistics, University of North Carolina at Chapel Hill · Department of Biostatistics, University of California, Los Angeles

  20. Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling

    Oct 7, 2026Daniel Paulin, Ádám Jung, András A. BenczúrConditional Distribution EstimationDensity Estimation

    Nanyang Technological University · Eötvös Lor´and University (ELTE) HUN-REN Institute for Computer Science and Control (SZTAKI)

  21. Generalized Matheron Variational Implicit Processes

    Oct 6, 2026Luis A. Ortega, Andrés R. Masegosa, Thomas D. NielsenBayesian InferenceGaussian Processes

    Aalborg University

  22. Gaussian Universality and Its Breakdown in Tensor-Network Machine Learning

    Oct 5, 2026Shi-Tuan Wang, Zidu Liu, Li-Wei YuTensor NetworksGaussian Processes

    Theoretical Physics Division, Chern Institute of Mathematics and LPMC, Nankai University, Tianjin 300071, China · Max Planck Institute of Quantum Optics, 85748 Garching, Germany

  23. Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions

    Oct 5, 2026Wei-Ting Tang, Madhav Muthyala, Joel A. PaulsonBayesian OptimizationGaussian Processes

    Department of Chemical and Biological Engineering University of Wisconsin–Madison Madison, WI 53706, USA

  24. GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization

    Sep 28, 2026Jintao Wei, Chenxi Li, Songhao WangBayesian OptimizationGaussian Processes

    College of Business, Southern University of Science and Technology Shenzhen, China

  25. What Does a Stream Model Buy You in Flow Matching?

    Sep 28, 2026Jian XuFlow MatchingConditional Flow Matching

    RIKEN

  26. Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization

    Sep 26, 2026Ashwin Renganathan, Peter BachmanBayesian OptimizationMulti-Objective Optimization

    Aerospace Engineering Pennsylvania State University University Park, PA 16802 · Institute of Computational and Data Sciences (ICDS) Pennsylvania State University University Park, PA 16802

  27. Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization

    Sep 26, 2026Zikai XieMulti-Output Gaussian ProcessesBayesian Optimization

    State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei, Anhui 230000, China

  28. Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisation

    Sep 24, 2026Fangqin Zhou, Joaquin VanschorenUncertainty QuantificationMulti-Task Learning

    Eindhoven University of Technology

  29. Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices

    Sep 24, 2026Yanchuang Cao, Jun Liu, Tengchao Yu +1Gaussian ProcessesGaussian Process Regression

    Institute of Applied Physics and Computational Mathematics, Beijing 100094, China

  30. Context-Continuous Preference Learning for Exoskeleton Personalization

    Sep 23, 2026Sunin Baek, Sungwoo Park, Daekyum KimGaussian ProcessesUser Preference Modeling

    School of Mechanical Engineering, Korea University, Seoul 02841, South Korea