Gaussian Process

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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.

144 papers

Latest in Gaussian Process

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  1. Online Gradient Computation for Warping Gaussian Process Transformations

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

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

    Sep 14, 2026Yean Hoon OngBayesian OptimizationGaussian Process

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

    Sep 11, 2026Jiaming Zhong, Reza Valiollahi Mehrizi, Mohammad Pirani +4Model Predictive ControlAgentic Control

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

    Sep 11, 2026A K M Nadimul Haque, Sheila Sutjipto, Marc G. Carmichael +1Obstacle AvoidanceHuman-To-Robot Transfer

  5. 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

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

    Sep 7, 2026Ondřej Sojka, Petr SojkaHyperparameterBayesian Optimization

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

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

  8. 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

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

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

  10. Recursive Gaussian Processes and the Bayesian Brain

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

  11. PIKS: Universal Physics-Informed Kernel Methods

    Jul 29, 2026Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1Reproducing Kernel Hilbert SpacesMultiscale Dynamics

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

    Jul 28, 2026Daniel Kua, Yan SongGenerative ModelsGaussian Process

  13. Normalizing Flows to Reconstruct Pseudo-PDFs

    Jul 28, 2026Yamil Cahuana Medrano, Kostas OrginosNormalizing FlowsReconstruction-Driven Framework

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

    Jul 26, 2026Jaewook Lee, Ethan Errington, Christian D. Lorenz +1Bayesian OptimizationSurrogate Modeling

  15. Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities

    Jul 25, 2026Jeremy OvadiaGaussian ProcessNew Satellites

  16. Smooth Neural Point Processes via B-Splines

    Jul 23, 2026Michele Bellomo, Riccardo Ramaschi, Alberto Dolara +1Determinantal Point ProcessB-Spline

  17. Adaptive Bayesian Online Learning via Expert Aggregation

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

  18. 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

  19. 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

  20. Hierarchical Bayesian Quadrature

    Jul 12, 2026Tim Weiland, Toni Karvonen, Philipp HennigBayesian Inference ApproachesGaussian Process

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

    Jul 11, 2026Yash Bagla, Jason SchneiderData-DrivenGaussian Process

  22. Deep Gaussian Processes on Directed Acyclic Graphs

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

  23. Pitfalls and Remedies for Multi-Task Bayesian Optimization

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

  24. Quantitative Gaussian-Process limits of Tensor Programs

    Jul 7, 2026Andrea Agazzi, Eloy Mosig García, Dario TrevisanGaussian ProcessWasserstein Distance

  25. How Many Initial Points Does Bayesian Optimization Need?

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

  26. 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

  27. Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

    Jul 1, 2026Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa JoseQuantum KernelsVariational Quantum Circuits

  28. Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

    Jun 30, 2026Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett +3Symbolic RegressionGenetic Algorithms

  29. Sequential sparse Gaussian process quantile regression

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

  30. Dynamic Gaussian Processes and the Vanilla-SPDE Exchange

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

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

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

  32. Online Data Selection for Instruction Tuning via Gaussian Processes

    Jun 29, 2026Jun Wang, Quoc Phong Nguyen, Julien Monteil +1Instruction TuningInstruction-Tuned Large Language Models

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

    Jun 27, 2026Geoffrey Davis, Ashwin RenganathanAerodynamicsSurrogate Modeling

  34. Active Quantum Kernel Acquisition for Gaussian Process Regression

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

  35. 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

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

    Jun 22, 2026Runzhe Liu, Zihao Wang, Wenbo Yang +1Chemical Process SystemsGaussian Process

  37. 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 Inference Approaches

  38. Generative Robust Optimisation

    Jun 21, 2026Yuhui Yin, Vassilis M. CharitopoulosPose UncertaintyMulti-Objective Optimization

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

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

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

    Jun 20, 2026Junzhe Zhang, Jingyuan Chen, Elias BareinboimLatent ConfoundersHeterogeneous Treatment Effects

  41. Orthogonal Discrepancy Kernels for Learning with Partial Physics

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

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

    Jun 18, 2026Mohammad Ali, Nithin Senthur Kumar, Eric J. Barth +1Continuum RobotsHamiltonian Dynamics

  43. Volterra Generative Models

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

  44. Differential Privacy of Gaussian Process Posterior Sampling

    Jun 16, 2026Tomasz MaciazekDifferential PrivacyGaussian Process

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

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

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

    Jun 16, 2026Oriol Vendrell-Gallart, Nima Negarandeh, Ramin BostanabadNeural OperatorsUncertainty Quantification

  47. Graphical conditional generative modeling for digital twin modeling

    Jun 15, 2026Zongren Zou, Théo Bourdais, Ricardo Baptista +1Digital TwinsSurrogate Modeling

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

    Jun 15, 2026Rajasree Sarkar, Arunava Banerjee, Sathya Aswath Govind Raju +4Turbofan EnginesElectric Vehicles

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

    Jun 14, 2026Tomoya WakayamaTest-Time AdaptationDistribution Shifts

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

    Jun 12, 2026Aleem Khan, Nicholas AndrewsUnsupervised DetectorsMachine-Generated Text Detection

  51. Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

    Jun 10, 2026Handi Zhang, Adrienne M. Propp, Brooks Kinch +2Surrogate ModelingReduced-Order Models

  52. 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

  53. 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

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

    Jun 7, 2026Yilin Zheng, Haowei Wang, Szu Hui Ng +1Bayesian OptimizationPocket-Conditioned Diffusion

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

    Jun 6, 2026Mark Kozdoba, Shie MannorGaussian ProcessBayesian Neural Networks

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

    Jun 6, 2026Yunbei XuGaussian ProcessMinimax

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

    Jun 5, 2026Taisei Saito, Koretaka Ogata, Takafumi HiroiOut-Of-Distribution Detection PerformanceContrastive Language-Image Pre-Training

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

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

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

    Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Gaussian ProcessHigh-Dimensional

  60. Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction

    Jun 4, 2026Jaeyeong Lee, Wonmo Koo, Heeyoung KimVessel Trajectory PredictionNeural Ordinary Differential Equations