Gaussian Process Regression

Also known as GP

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Overview

Gaussian process regression (GPR) places a probability distribution over functions, with kernels encoding assumptions such as smoothness, periodicity, or shared structure; conditioning on observations yields predictions and uncertainty, especially useful when data are scarce or decisions depend on confidence. Research develops richer models—including nonstationary, additive, multi-output, deep, and physics-informed GPs—and applies them to time series, spatial fields, dynamical systems, scientific surrogates, and Bayesian optimization. Because exact inference scales cubically with sample count, scalable approaches use inducing points and variational inference, nearest-neighbor or Vecchia approximations, sparse and low-rank kernels, and streaming or distributed updates. A central tension is balancing expressive models and calibrated uncertainty against computational cost and sensitivity to kernel and hyperparameter choices; reliable uncertainty under misspecification and scalable high-dimensional inference remain open challenges.

Latest papers

CardsList

111 papers14 papers in the last four weeks, down 7% on the four weeks before. 0.1% of all new papers.

  1. Data-Driven or Model-Constrained? Comparing Airflow Estimation Methods under Sparse Sensing

    Oct 29, 2026Dominik Aigner, Thomas Wiedemann, Javier Monroy +1Physics-Informed MLComputational Fluid Dynamics

    Technical University of Munich · German Aerospace Center · University of Málaga

  2. A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts

    Oct 26, 2026Zhen-Fan Wang, Kai Zhang, Xianping Du +3Surrogate ModelingGaussian Process Regression

    State Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China · School of Ocean Engineering and Technology, Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China · Shenzhen Research Institute of Shanghai Jiao Tong University, Shenzhen, 518063, China +1

  3. Input Bias Correction in Gaussian Process Regression

    Oct 22, 2026Muzaffar Qureshi, Tochukwu E. Ogri, Trivikram Satharasi +2Gaussian Process RegressionUncertainty Calibration

    Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA · Torch Technologies, Shalimar, FL, USA

  4. Continuous-depth Deep Gaussian Processes

    Oct 22, 2026Ying Li, Sanyou Wu, Zi Yang +6Deep Gaussian ProcessesGaussian Process Regression

    National University of Singapore · Columbia University · The University of Hong Kong +3

  5. Geometry-Aware Block Selection for Sparse Variational Gaussian Processes

    Oct 20, 2026Matteo HoxhaSparse Gaussian ProcessesGaussian Process Regression

    Nanyang Technological University, Singapore

  6. A Probabilistic, Rotation-Preserving Kernel Operator Surrogate for Continuum-Robot Design Spaces

    Oct 19, 2026Branden Frieden, Alan Kuntz, Varun ShankarNeural Surrogate ModelingContinuum Robot Design

    The Kahlert School of Computing at the University of Utah, Salt Lake City, UT 84112, USA. · The Departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University, Nashville, TN, 37203, USA.

  7. 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

  8. Fully sequential Gaussian process regression for preference learning

    Oct 14, 2026Ilya O. RyzhovBayesian OptimizationGaussian Process Regression

  9. 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

  10. Exploiting Gradients in Bayesian Inference of Expensive Simulators

    Oct 8, 2026Šimon Soldát, Václav ŠmídlSimulation-Based InferenceBayesian Optimization

    Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic · Institute of Plasma Physics of the Czech Academy of Sciences, Prague, Czech Republic

  11. Derivative Gaussian Processes on a Two-Direction Budget

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

    University of Wisconsin–Madison

  12. Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty

    Oct 6, 2026Amir Mohammad Mahfoozi, Zi Yang, Ying Li +1Transformer AttentionAttention Mechanisms

    Department of Computer Engineering, Sharif University of Technology · School of Computing and Data Science, The University of Hong Kong

  13. Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

    Oct 5, 2026Yaniv Yacoby, Weiwei Pan, Hope Neveux +4Latent Variable ModelsSuicide Risk Modeling

    Wellesley College · Harvard University · Harvard University Harvard Medical School Massachusetts General Hospital

  14. 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

  15. Predictively Oriented Gaussian Process Posteriors

    Oct 2, 2026Callum Lau, Jeremias Knoblauch, Louis SharrockGaussian Process RegressionUncertainty Quantification

    University College London

  16. Real-time Event-camera Stereo Visual Odometry via Keytime Gaussian Process Regression

    Oct 1, 2026Nikan Nobari, Jonathan D. GammellVisual OdometryStereo Vision

    Estimation, Search, and Planning (ESP) Research Group, Queen’s University, Kingston ON, Canada.

  17. Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection

    Sep 27, 2026Jia CaiFeature SelectionGaussian Process Regression

    Department of Statistics, George Mason University, Fairfax, Virginia/USA

  18. Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection

    Sep 27, 2026Jia CaiFDR ControlFeature Selection

    Department of Statistics, George Mason University Fairfax, Virginia, USA

  19. 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

  20. 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

  21. 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

  22. Resource-Efficient Distributed Recursive Gaussian Processes

    Sep 22, 2026Josephine King, Ali Emre Balci, Raj Thilak RajanDistributed OptimizationGaussian Processes

    Signal Processing Systems, Faculty of EEMCS, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands

  23. On Basis Function Selection for Sparse Gaussian Process Regression

    Sep 22, 2026Marnix Van Soom, Ivan De BoiFeature SelectionSparse Gaussian Processes

    AI Lab Vrije Universiteit Brussel · InViLab University of Antwerp

  24. Triply-Scalable Equivariant Gaussian Process Modeling

    Sep 17, 2026Tim Steinert, David GinsbourgerSparse Gaussian ProcessesGaussian Processes

    Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland

  25. COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression

    Sep 17, 2026Zewen Yang, Xiaobing Dai, Zhenxiao Yin +3Online System IdentificationRobot State Estimation

    Chair of Robotics and Systems Intelligence (RSI), Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), 80992 Munich, Germany · Technical University of Munich, 80333 Munich, Germany · The Hong Kong University of Science and Technology (Guangzhou) +2

  26. Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems

    Sep 16, 2026Xianjian Xie, Hao YanGaussian ProcessesFederated Learning

    Arizona State University

  27. A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

    Sep 16, 2026Marcus M. Noack, Maher B. Alghalayini, Mark D. RisserKernel MethodsGaussian Process Regression

    Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA · Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

  28. Gaussian Processes for Modelling Spatial Fields with Robot Swarms

    Sep 15, 2026Guillermo Legarda Herranz, Gianpiero Francesca, Mauro BirattariSwarm RoboticsGaussian Process Regression

    IRIDIA, Universit´e libre de Bruxelles, 1050 Brussels, Belgium · Toyota Motor Europe

  29. Online Gradient Computation for Warping Gaussian Process Transformations

    Sep 15, 2026Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-LozanoGaussian ProcessesOnline Learning

    SIGIPRO Department, Simula Metropolitan Center for Digital Engineering, Oslo, Norway · SURE-AI Center, Simula Research Laboratory, Oslo, Norway · Institute of Science Tokyo, Yokohama, Japan

  30. Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration

    Sep 14, 2026Yean Hoon OngBayesian OptimizationGaussian Processes

    University College London London, United Kingdom