Gaussian Processes

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10 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.

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  1. Quantitative Gaussian-Process limits of Tensor Programs

    Jul 7, 2026Andrea Agazzi, Eloy Mosig García, Dario TrevisanGaussian Processes

  2. Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data

    Jul 2, 2026Yue Zhang, Nandini Amit Gadhia, Georgios Karagiannis +1Gaussian ProcessesKernel Methods

    Department of Mathematical Sciences Durham University Durham, United Kingdom · STFC Hartree Centre University of Liverpool Liverpool, United Kingdom · STFC Hartree Centre Daresbury, United Kingdom

  3. Sequential sparse Gaussian process quantile regression

    Jun 30, 2026Hugo Nicolas, Olivier Le MaîtreQuantile RegressionUncertainty Quantification

    PLATON, CMAP · Inria, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France · CNRS, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France

  4. Dynamic Gaussian Processes and the Vanilla-SPDE Exchange

    Jun 30, 2026Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2Efficient InferenceGaussian Processes

    School of Mathematical Sciences, Lancaster University. · School of Mathematics and Statistics, The University of New South Wales. · School of Physics, Mathematics and Computing, the University of Western Australia.

  5. Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves

    Jun 30, 2026Ahmadreza Chokhachian, V. Roshan Joseph, Yu DingGaussian ProcessesGaussian Process Regression

    H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology

  6. A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

    Jun 27, 2026Geoffrey Davis, Ashwin RenganathanUncertainty QuantificationGaussian Processes

    Undergraduate research assistant, Aerospace Engineering, The Pennsylvania State University, University Park, PA 16802 · Assistant professor, Aerospace Engineering and the Institute of Computational and Data Science (ICDS), The Pennsylvania State University, University Park, PA 16802

  7. An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes

    Jun 24, 2026Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-LobatoDeep Learning OptimizationPosterior Collapse

    Department of Computer Science and Artificial Intelligence Universidad de Granada Granada, Spain · Department of Quantitative Methods CUNEF Universidad Madrid, Spain · Machine Learning Group at Universidad Autónoma de Madrid +1

  8. Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

    Jun 22, 2026Qi Li, Yuliang Huang, Shaheer U. Saeed +7Gaussian ProcessesMedical Image Segmentation

    UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London, London, U.K. · Centre for Bioengineering, Digital Environment Research Institute, School of Engineering and Materials Science, Queen Mary University of London, London, U.K. · Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, U.K. +4

  9. Differential Privacy of Gaussian Process Posterior Sampling

    Jun 16, 2026Tomasz MaciazekGaussian ProcessesPrivacy-Preserving ML

    School of Mathematics, University of Bristol

  10. RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping

    Jun 16, 2026Bingbing Zhang, Huan Yin, Yang Xu +4Simultaneous Localization and MappingGaussian Processes

    State Key Laboratory of Ocean Sensing, Zhejiang University, China · Interdisciplinary Student Training Platform for Marine areas, Zhejiang University, Hangzhou, China · Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong +3

  11. How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs

    Jun 6, 2026Mark Kozdoba, Shie MannorGaussian Processes

    Technion, IIT · NVIDIA

  12. Pointwise Complexity for Gaussian Fields: Upper Envelopes, Algorithmic Lower Bounds, and Separation

    Jun 6, 2026Yunbei XuGaussian ProcessesMinimax Estimation

    National University of Singapore

  13. 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

  14. Spectral Anatomy of Quantum Gaussian Process Kernels

    May 29, 2026Jian Xu, Chao Li, Guang Lin +4Quantum Machine LearningGaussian Processes

    1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1

  15. SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning

    May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianBayesian Neural NetworksGaussian Processes

    Department of Electrical and Computer Engineering, Texas A&M University, College Station, US · Department of Industrial and Systems Engineering, Texas A&M University, College Station, US

  16. Collaborative Navigation and Exploration with ββ-Sparse Gaussian Processes

    May 25, 2026Evangelos Psomiadis, Dipankar Maity, Panagiotis TsiotrasMulti-Robot SystemsRobot Navigation

    D. Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA, USA · Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, NC, USA

  17. Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes

    May 25, 2026Maria Bånkestad, Sanna Jarl, Jens SjölundBayesian OptimizationGaussian Processes

    RISE Research Institutes of Sweden, Stockholm, Sweden · Uppsala University, Uppsala, Sweden

  18. 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

  19. Conditioning Gaussian Processes on Almost Anything

    May 20, 2026Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite +5Gaussian ProcessesConditional Distribution Estimation

    Lancaster University · University of New South Wales · University of Cambridge +1

  20. Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

    May 19, 2026Aurélien Pion, Emmanuel VazquezPost-Hoc CalibrationBayesian Optimization

    Transvalor S.A., Biot, France · Univ. Paris-Saclay, CNRS, CentraleSup´elec, L2S, Gif-sur-Yvette, France

  21. 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

  22. Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization

    May 13, 2026Jannis Lübsen, Annika EichlerBayesian OptimizationGaussian Processes

    Institute of Control Systems, Hamburg University of Technology, Hamburg, Germany · Institute of Control Systems, Hamburg University of Technology · Deutsches Elektronen-Synchrotron, Hamburg, Germany

  23. Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

    May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberTime Series GenerationGaussian Processes

    Josef Ressel Centre for Intelligent and Secure Industrial Automation Salzburg University of Applied Sciences, Salzburg, Austria

  24. Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes

    May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Dynamical SystemsBayesian Inference

    Aalto University · Inria SISTM team · Univ. Bordeaux, INSERM BPH, U1219, Inria SISTM team, VRI, France

  25. Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not

    May 12, 2026Kumbha Nagaswetha, Rabi PathakSurrogate ModelingBayesian Optimization

  26. 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

  27. Scalable Gaussian process inference via neural feature maps

    May 11, 2026Anthony StephensonGaussian ProcessesKernel Methods

    School of Mathematics University of Bristol

  28. Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs

    May 9, 2026Connor Holmes, Sven Lilge, Zi Cong Guo +2Gaussian ProcessesRobot State Estimation

    University of Toronto · Georgia Institute of Technology

  29. Permutation-preserving Functions and Neural Vecchia Covariance Kernels

    May 6, 2026Jian Cao, Nian Liu, Ying LinGaussian ProcessesKernel Methods

    Department of Mathematics University of Houston Houston, TX 77047 · Department of Industrial and Systems Engineering University of Houston Houston, TX 77047

  30. Transformed Latent Variable Multi-Output Gaussian Processes

    May 6, 2026Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2Latent Variable ModelsGaussian Processes

    Department of Computer Science, University of Manchester, Manchester, UK · Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK