Gaussian Process Regression

Also known as GP

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 68

All topics

Exploiting Gradients in Bayesian Inference of Expensive Simulators
Oct 8, 2026Šimon Soldát, Václav ŠmídlSimulation-Based Inference Bayesian 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
Derivative Gaussian Processes on a Two-Direction Budget
Oct 7, 2026Hyunseok Seung, Matthias KatzfussGaussian Processes Gaussian Process Regression
University of Wisconsin–Madison
Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty
Oct 6, 2026Amir Mohammad Mahfoozi, Zi Yang, Ying Li +1Transformer Attention Attention Mechanisms
Department of Computer Engineering, Sharif University of Technology · School of Computing and Data Science, The University of Hong Kong
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 Models Suicide Risk Modeling
Wellesley College · Harvard University · Harvard University Harvard Medical School Massachusetts General Hospital
Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions
Oct 5, 2026Wei-Ting Tang, Madhav Muthyala, Joel A. PaulsonBayesian Optimization Gaussian Processes
Department of Chemical and Biological Engineering University of Wisconsin–Madison Madison, WI 53706, USA
Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection
Sep 27, 2026Jia CaiFeature Selection Gaussian Process Regression
Department of Statistics, George Mason University, Fairfax, Virginia/USA
Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection
Sep 27, 2026Jia CaiFDR Control Feature Selection
Department of Statistics, George Mason University Fairfax, Virginia, USA
Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisation
Sep 24, 2026Fangqin Zhou, Joaquin VanschorenUncertainty Quantification Multi-Task Learning
Eindhoven University of Technology
Resource-Efficient Distributed Recursive Gaussian Processes
Sep 22, 2026Josephine King, Ali Emre Balci, Raj Thilak RajanDistributed Optimization Gaussian Processes
Signal Processing Systems, Faculty of EEMCS, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands
On Basis Function Selection for Sparse Gaussian Process Regression
Sep 22, 2026Marnix Van Soom, Ivan De BoiFeature Selection Gaussian Process Regression
AI Lab Vrije Universiteit Brussel · InViLab University of Antwerp
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 Identification Robot 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
Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems
Sep 16, 2026Xianjian Xie, Hao YanGaussian Processes Federated Learning
Arizona State University
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 Methods Gaussian 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
Gaussian Processes for Modelling Spatial Fields with Robot Swarms
Sep 15, 2026Guillermo Legarda Herranz, Gianpiero Francesca, Mauro BirattariSwarm Robotics Gaussian Process Regression
IRIDIA, Universit´e libre de Bruxelles, 1050 Brussels, Belgium · Toyota Motor Europe
Online Gradient Computation for Warping Gaussian Process Transformations
Sep 15, 2026Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-LozanoGaussian Processes Online 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
Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration
Sep 14, 2026Yean Hoon OngBayesian Optimization Gaussian Processes
University College London London, United Kingdom
A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
Aug 12, 2026Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko +2Gaussian Process Regression
Eindhoven University of Technology & Lazy Dynamics B.V., the Netherlands · Eindhoven University of Technology, Eindhoven, the Netherlands · Lazy Dynamics B.V., the Netherlands
Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization
Jul 26, 2026Jaewook Lee, Ethan Errington, Christian D. Lorenz +1Multi-Fidelity Optimization Surrogate Modeling
Department of Engineering, King’s College London, London, WC2R 2LS, United Kingdom.
Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities
Jul 25, 2026Jeremy OvadiaUncertainty Quantification Gaussian Processes
Sequoia Research Corporation 2875 Temple Ave Signal Hill, CA, 90755, USA
Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
Jul 23, 2026Khalid El-Darymli, Christoph H. Gierull, Katerina Biron +1Uncertainty Quantification Synthetic Aperture Radar
Defence Research and Development Canada, Ottawa, Ontario, Canada · Memorial University, St. John’s, NL, Canada
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Jul 21, 2026Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1Bayesian Inference Gaussian Processes
Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH · Department of Mathematics, Purdue University, West Lafayette, IN
Hierarchical Bayesian Quadrature
Jul 12, 2026Tim Weiland, Toni Karvonen, Philipp HennigUncertainty Quantification Bayesian Inference
Tübingen AI Center, University of Tübingen, Tübingen, Germany · School of Engineering Sciences, Lappeenranta–Lahti University of Technology LUT, Lappeenranta, Finland
Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
Jul 8, 2026Sarah Grewe, Jörg FrochteSurrogate Modeling Residual Learning
Bochum University of Applied Sciences Germany
Sequential sparse Gaussian process quantile regression
Jun 30, 2026Hugo Nicolas, Olivier Le MaîtreQuantile Regression Uncertainty 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
Dynamic Gaussian Processes and the Vanilla-SPDE Exchange
Jun 30, 2026Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2Efficient Inference Gaussian 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.
Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves
Jun 30, 2026Ahmadreza Chokhachian, V. Roshan Joseph, Yu DingGaussian Processes Gaussian Process Regression
H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology
A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
Jun 27, 2026Geoffrey Davis, Ashwin RenganathanUncertainty Quantification Gaussian 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
Active Quantum Kernel Acquisition for Gaussian Process Regression
Jun 27, 2026Jian Xu, Artur Miroszewski, John Paisley +2Quantum Kernel Methods Gaussian Process Regression
RIKEN iTHEMS · RIKEN AIP · European Space Agency +2
Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
Jun 22, 2026Runzhe Liu, Zihao Wang, Wenbo Yang +1Markov Chain Monte Carlo Bayesian Optimization
State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian University of Technology, Dalian, 116024, China · Dalian Key Laboratory of Intelligent Chemistry, CR Belt and Road Joint Laboratory on Intelligent Chemistry and Advanced Materials of Liaoning Province, School of Chemistry, Dalian University of Technology, Dalian, 116024, China
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 +2Neural Surrogate Modeling Amortized Inference
Department of Statistical Sciences, University of Toronto · Data Sciences Institute, University of Toronto · School of the Environment, University of Toronto +3

Oct 8, 2026, cs.LGJ/K move · Enter open · S save

Exploiting Gradients in Bayesian Inference of Expensive Simulators

Šimon Soldát, Václav Šmídl

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

Simulation-Based Inference Bayesian Optimization Surrogate-Assisted Optimization Active Learning Gaussian Process Regression

Open paper AskPDF arXiv

Simulators based on differential equations are ubiquitous in science and engineering. They are often used in simulation-based inference to evaluate the posterior distribution of the input parameters based on real-world observations of the simulator outputs. However, inference becomes challenging when individual simulator evaluations are computationally expensive. In such cases, a Bayesian optimization-based active learning approach with Gaussian process surrogate models has been used to maximize the information obtained from a limited simulation budget. Recently, gradients of simulator outputs with respect to input parameters have become increasingly available, yet they are rarely exploited for inference. Even though we only need to learn the simulator input-output relationship, gradient information can provide an additional valuable signal to guide the active learning procedure. This is of particular interest in the case of expensive simulators, when sample efficiency is crucial. In this paper, we demonstrate how incorporating gradient information into the Gaussian process surrogate accelerates Bayesian optimization-based inference under a limited simulation budget. Our results show significant improvement in convergence speed from using gradient information. For reverse-mode differentiation, the inference efficiency gains are maintained when accounting for the additional computational cost. In contrast, for forward-mode differentiation, the inference speed-up does not outweigh the computational costs. These results indicate that gradient-enhanced surrogates are beneficial primarily in problems where the number of parameters exceeds the output dimensionality, where reverse-mode differentiation is efficient.

Momentum

Related topics

Latest papers 68