Gaussian Process

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6 papers in the last 28 days · 0.2% of indexed attention

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Period ending 2026-09-21

8 new papers

A weekly snapshot of new work published in Gaussian Process.

Period ending 2026-09-14

4 new papers

A weekly snapshot of new work published in Gaussian Process.

Period ending 2026-09-07

3 new papers

A weekly snapshot of new work published in Gaussian Process.

143 papers

Latest in Gaussian Process

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

    Sep 17, 2026Zewen Yang, Xiaobing Dai, Zhenxiao Yin +3Gaussian ProcessState Estimation

  2. Online Adaptive Kernel Mixing for Gaussian Process Decision Making

    Sep 17, 2026Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy +1Gaussian ProcessKernel Method

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

    Sep 16, 2026Xianjian Xie, Hao YanGaussian ProcessDistributed Optimization

  4. 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 MethodDistance

  5. Gaussian Processes for Modelling Spatial Fields with Robot Swarms

    Sep 15, 2026Guillermo Legarda Herranz, Gianpiero Francesca, Mauro BirattariSwarmsGaussian Process

  6. Online Gradient Computation for Warping Gaussian Process Transformations

    Sep 15, 2026Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-LozanoGaussian ProcessWasserstein Gradient Flows

  7. Physics Informed Random Feature Neural Networks for Solving PDEs

    Sep 14, 2026Chi-An Chen, Chunyang Liao, Ming ZhongPartial Differential EquationsKernel Method

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

    Sep 14, 2026Yean Hoon OngBayesian OptimizationGaussian Process

  9. Learning Agent-based Model Predictive Control for Holistic Vehicle Performance

    Sep 11, 2026Jiaming Zhong, Reza Valiollahi Mehrizi, Mohammad Pirani +4Model Predictive ControlMulti-Agent Reinforcement Learning

  10. Safety-aware Skill Adaptation for Reinforcement Learning in Dynamic Environments

    Sep 11, 2026A K M Nadimul Haque, Sheila Sutjipto, Marc G. Carmichael +1Obstacle AvoidanceEnvironment

  11. Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

    Sep 8, 2026Yousra El-Bachir, Frederik Van der Veken, Davide di Croce +4Gaussian ProcessBayesian Neural Networks

  12. The Art of Hierarchical Competing Patterns: Gaussian Process Optimization of Hyphenation

    Sep 7, 2026Ondřej Sojka, Petr SojkaHyperparameter OptimizationPrompt Optimization

  13. No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels

    Sep 2, 2026Edvin Ketabati Augustinsson, Robert A. BridgesBayesian OptimizationGaussian Process

  14. Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization

    Aug 17, 2026Shion Takeno, Shogo IwazakiGeneralized Linear BanditLinear Regret

  15. A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

    Aug 12, 2026Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko +2Gaussian ProcessFactor Graphs

  16. ErgoSurf: Ergodic Control for the Coverage of Unknown Surfaces

    Aug 6, 2026Stefan Schneyer, Timo Bachmann, Maged Iskandar +4High-Fidelity Surface ReconstructionContact-Rich Manipulation

  17. Recursive Gaussian Processes and the Bayesian Brain

    Aug 1, 2026Moumita Das, Dipanjan Ray, Sourabh BhattacharyaPredictive CodingGaussian Process

  18. Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?

    Jul 28, 2026Daniel Kua, Yan SongGenerative ModelsGaussian Process

  19. Normalizing Flows to Reconstruct Pseudo-PDFs

    Jul 28, 2026Yamil Cahuana Medrano, Kostas OrginosNormalizing FlowsEquivariant Neural Networks

  20. Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

    Jul 26, 2026Jaewook Lee, Ethan Errington, Christian D. Lorenz +1Bayesian OptimizationGaussian Process

  21. Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities

    Jul 25, 2026Jeremy OvadiaGaussian Process

  22. Adaptive Bayesian Online Learning via Expert Aggregation

    Jul 22, 2026Jungbin Jun, Ilsang OhnLearning-Augmented AlgorithmsBayesian

  23. A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling

    Jul 21, 2026Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1Gaussian ProcessDimensionality Reduction

  24. Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography

    Jul 15, 2026Boyuan Deng, Kshitiz Upadhyay, Michael ShieldsWaveIll-Posed Inverse Problem

  25. Hierarchical Bayesian Quadrature

    Jul 12, 2026Tim Weiland, Toni Karvonen, Philipp HennigBayesian QuadratureBayesian

  26. Fast Data-Driven Modeling of Hydraulic Clutch Control Pressure with Latch-State Classification and Gaussian Process Regression

    Jul 11, 2026Yash Bagla, Jason SchneiderSplit Hopkinson Pressure Bar ConfigurationData-Driven

  27. Deep Gaussian Processes on Directed Acyclic Graphs

    Jul 10, 2026Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen +1Directed Acyclic GraphGaussian Process

  28. Pitfalls and Remedies for Multi-Task Bayesian Optimization

    Jul 10, 2026Carl Hvarfner, Sam Daulton, Max Balandat +1Bayesian OptimizationGaussian Process

  29. Quantitative Gaussian-Process limits of Tensor Programs

    Jul 7, 2026Andrea Agazzi, Eloy Mosig García, Dario TrevisanGaussian ProcessInfinite-Width Gaussian Limit

  30. How Many Initial Points Does Bayesian Optimization Need?

    Jul 5, 2026Mujin Cheon, James Odgers, Dong-Yeun Koh +1Bayesian OptimizationThompson Sampling

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

    Jul 2, 2026Yue Zhang, Nandini Amit Gadhia, Georgios Karagiannis +1Multi-Omic FrameworkExtreme Class Imbalance

  32. Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

    Jul 1, 2026Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa JoseQuantum KernelsQuantum Advantage

  33. Sequential sparse Gaussian process quantile regression

    Jun 30, 2026Hugo Nicolas, Olivier Le MaîtreGaussian ProcessQuantile Regression

  34. Dynamic Gaussian Processes and the Vanilla-SPDE Exchange

    Jun 30, 2026Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2Gaussian Process3D Gaussian Splatting

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

    Jun 30, 2026Ahmadreza Chokhachian, V. Roshan Joseph, Yu DingOffshore Wind TurbineGaussian Process

  36. Online Data Selection for Instruction Tuning via Gaussian Processes

    Jun 29, 2026Jun Wang, Quoc Phong Nguyen, Julien Monteil +1Instruction TuningOnline Data Selection

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

    Jun 27, 2026Geoffrey Davis, Ashwin RenganathanAerodynamicsCalibrated Uncertainty

  38. Active Quantum Kernel Acquisition for Gaussian Process Regression

    Jun 27, 2026Jian Xu, Artur Miroszewski, John Paisley +2Quantum KernelsGaussian Process

  39. 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 +7Medical Image SegmentationMedical Imaging Datasets

  40. Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery

    Jun 22, 2026Runzhe Liu, Zihao Wang, Wenbo Yang +1Gaussian ProcessMarkov Chain Monte Carlo

  41. Scalable Bayesian Additive Models for Stellar Flare Detection via Amortized Gaussian Process Inference and Hidden Markov Models

    Jun 21, 2026Rodrigo Herrera, Vianey Leos-Barajas, Gwendolyn Eadie +2Gaussian ProcessBayesian

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

    Jun 21, 2026Yaozhong Shi, Zachary E. Ross, Yisong YuePosterior SamplingIll-Posed Inverse Problem

  43. Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding

    Jun 20, 2026Junzhe Zhang, Jingyuan Chen, Elias BareinboimCausal InferencesLatent Confounders

  44. Orthogonal Discrepancy Kernels for Learning with Partial Physics

    Jun 19, 2026Swapnil Manna, Timothy J. Rogers, Lawrence BullSystem IdentificationDiscrepancy

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

    Jun 18, 2026Mohammad Ali, Nithin Senthur Kumar, Eric J. Barth +1Soft RobotsDynamics Models

  46. Volterra Generative Models

    Jun 16, 2026Yusen Jia, Bingyan HanScore-Based Diffusion ModelGenerative Models

  47. Differential Privacy of Gaussian Process Posterior Sampling

    Jun 16, 2026Tomasz MaciazekDifferential PrivacyGaussian Process

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

    Jun 16, 2026Bingbing Zhang, Huan Yin, Yang Xu +4MappingGaussian Process

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

    Jun 16, 2026Oriol Vendrell-Gallart, Nima Negarandeh, Ramin BostanabadNeural OperatorsGaussian Process

  50. Data-driven Control with Real-time Uncertainty Compensation for Multi-Fuel Engines

    Jun 15, 2026Rajasree Sarkar, Arunava Banerjee, Sathya Aswath Govind Raju +4Combustion ControlGaussian Process

  51. A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt

    Jun 14, 2026Tomoya WakayamaDistribution ShiftsBayesian

  52. Gradient boosting for extremes: sampling theory and application to insurance

    Jun 12, 2026Stéphane Lhaut, Olivier LopezGradient Boosted TreeGaussian Process

  53. Non-Parametric Machine Text Detection via Multi-View Gaussian Processes

    Jun 12, 2026Aleem Khan, Nicholas AndrewsDetection FrameworkMachine-Generated Text Detection

  54. Vehicle Prediction Model for Enhanced MPC Path Tracking in Formula Student Driverless

    Jun 9, 2026Sebastian Baader, Tamara Bergerhoff, Pascal Meißner +1Model Predictive ControlModel Predictive Path Integral

  55. Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge

    Jun 7, 2026Liqiu Dong, Marta Zagórowska, Mehmet MercangözChemical Process SystemsBayesian Optimization

  56. Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models

    Jun 7, 2026Yilin Zheng, Haowei Wang, Szu Hui Ng +1Bayesian OptimizationBayesian

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

    Jun 6, 2026Mark Kozdoba, Shie MannorGaussian ProcessCentral Limit Theorem

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

    Jun 6, 2026Yunbei XuGaussian ProcessMinimax

  59. GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection

    Jun 5, 2026Taisei Saito, Koretaka Ogata, Takafumi HiroiOut-Of-Distribution DetectionFew-Shot Learning

  60. Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts

    Jun 5, 2026Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +1Bayesian OptimizationMulti-Objective Optimization