Gaussian Process Regression

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11 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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  1. Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

    Jun 21, 2026Yaozhong Shi, Zachary E. Ross, Yisong YueBayesian Inverse ProblemsPosterior Sampling

    California Institute of Technology

  2. Orthogonal Discrepancy Kernels for Learning with Partial Physics

    Jun 19, 2026Swapnil Manna, Timothy J. Rogers, Lawrence BullSemiparametric InferenceNonlinear System Identification

    IISER Pune, India · University of Sheffield, UK · University of Glasgow, UK

  3. Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

    Jun 18, 2026Mohammad Ali, Nithin Senthur Kumar, Eric J. Barth +1Dynamical SystemsPort-Hamiltonian Systems

    Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA · Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235, USA

  4. Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning

    Jun 16, 2026Oriol Vendrell-Gallart, Nima Negarandeh, Ramin BostanabadUncertainty QuantificationPDE Surrogate Modeling

    Department of Mechanical and Aerospace Engineering University of California, Irvine Irvine, CA 92617

  5. Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

    Jun 10, 2026Handi Zhang, Adrienne M. Propp, Brooks Kinch +2Uncertainty QuantificationPDE Surrogate Modeling

    Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA · Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA · Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA +1

  6. Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

    Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Latent Variable ModelsDimensionality Reduction

    University of Cambridge · University of Colorado Boulder · University of Oxford +1

  7. Scalable Derivative Gaussian Processes via Exact Gradient Reduction

    Jun 1, 2026Hyunseok Seung, Matthias KatzfussSurrogate ModelingGaussian Processes

    Department of Statistics · University of Wisconsin–Madison · Madison, WI, 53706

  8. On the Uncertainty Quantification Ability of Tabular Foundation Models

    May 31, 2026Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2Tabular Foundation ModelsUncertainty Quantification

    Department of Mechanical and Aerospace Engineering, University of California, Irvine · Department of Civil and Environmental Engineering, University of California, Irvine

  9. Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing

    May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohEpidemic ForecastingTime Series Forecasting

  10. Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise

    May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Surrogate ModelingUncertainty Quantification

    University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville, Tennessee 37996, USA · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. +2

  11. Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

    May 26, 2026Ujjwala VadrevuQuantitative FinanceGaussian Process Regression

  12. Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix

    May 26, 2026Kane Warrior, Dalia ChakrabartyBayesian InferenceCovariance Estimation

    Department of Mathematics University of York

  13. Onsager-Machlup Posterior Transport for Deep Gaussian Processes

    May 22, 2026Jian Xu, Delu Zeng, John Paisley +1Gaussian ProcessesVariational Inference

    RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1

  14. Aerodynamic force reconstruction using physics-informed Gaussian processes

    May 21, 2026Gledson Rodrigo Tondo, Igor Kavrakov, Guido MorgenthalPhysics-Informed MLInverse Problems

    Bauhaus-Universität Weimar Weimar, Germany · University of Cambridge Cambridge, United Kingdom

  15. Three Costs of Amortizing Gaussian Process Inference with Neural Processes

    May 20, 2026Robin YoungLatent Variable ModelsAmortized Inference

    University of Cambridge, Cambridge, UK

  16. Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport

    May 20, 2026Mehmet Yigit Balik, Harri LähdesmäkiLatent Dynamics ModelingConditional Generative Modeling

    Department of Computer Science, Aalto University, Espoo, Finland.

  17. DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift

    May 19, 2026Kieran Wood, Stefan Zohren, Stephen J. RobertsDistribution Shift RobustnessTime Series Forecasting

    Machine Learning Research Group, University of Oxford · Oxford-Man Institute, University of Oxford

  18. Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization

    May 18, 2026Taeyoung Yun, Woocheol Shin, Inhyuck Song +2Bayesian OptimizationLarge Language Model-Guided Optimization

    1Korea Advanced Institute of Science and Technology (KAIST)

  19. Lightweight Gaussian Process Inference in C++ on Metal and CUDA

    May 18, 2026Yu-Hsueh FangGPU Kernel OptimizationGPU Acceleration

    Department of Information Management, National Taiwan University · 2H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

  20. A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices

    May 17, 2026Ujjwala VadrevuQuantitative FinanceFinancial Time Series

  21. Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

    May 11, 2026Kane Warrior, Dalia ChakrabartyGaussian ProcessesLie Group Methods

    University of York, Department of Mathematics, York, United Kingdom

  22. Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

    May 11, 2026Jixiang Qing, Henry Moss, Matthias SachsActive LearningGaussian Process Regression

    MARS: Mathematics for AI in Real-world Systems, School of Mathematical Sciences, Lancaster University, Lancaster, LA1 4YF, United Kingdom.

  23. Online Sharp-Calibrated Bayesian Optimization

    May 11, 2026Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1Bayesian OptimizationUncertainty Calibration

    ELLIS Institute Finland · Aalto University · University of Manchester

  24. Multifidelity Gaussian process regression for solving nonlinear partial differential equations

    May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantSurrogate ModelingPDE Solving

    Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France

  25. Scalable Gaussian process inference via neural feature maps

    May 11, 2026Anthony StephensonGaussian ProcessesKernel Methods

    School of Mathematics University of Bristol

  26. Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids

    May 8, 2026Mads Greisen Højlund, August Smart Lykke-Møller, Henry Moss +1Kernel MethodsGaussian Process Regression

    Department of Chemistry Aarhus University · School of Mathematical Sciences Lancaster University

  27. Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations

    May 7, 2026Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2Latent Dynamics ModelingPhysics-Informed ML

    Leibniz Universität Hannover · Saarland University · Uppsala University

  28. Deep Kernel Learning for Stratifying Glaucoma Trajectories

    May 1, 2026Bruce Rushing, Angela Danquah, Alireza Namazi +2Clinical Outcome PredictionKernel Methods

    Research Computing, University of Virginia · School of Data Science, University of Virginia · School of Medicine, University of Virginia

  29. Provable and scalable quantum Gaussian processes for quantum learning

    Apr 30, 2026Jonas Jäger, Paolo Braccia, Pablo Bermejo +3Quantum Machine LearningQuantum Kernel Methods

    Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Computer Science and Institute of Applied Mathematics, University of British Columbia, Vancouver, V6T 1Z4 B.C., Canada · Stewart Blusson Quantum Matter Institute, Vancouver, V6T 1Z4 B.C., Canada +8

  30. Sequential Inference for Gaussian Processes: A Signal Processing Perspective

    Apr 30, 2026Daniel Waxman, Fernando Llorente, Petar M. DjurićGaussian ProcessesGaussian Process Regression

    1Basis Research Institute · 3Computing and Data Sciences Directorate, Brookhaven National Laboratory · Department of Electrical and Computer Engineering, Stony Brook University