Gaussian Process

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Period ending 2026-09-21

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144 papers

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Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
Jun 5, 2026Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +1Bayesian Optimization Multi-Objective Optimization
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Gaussian Process High-Dimensional Data
Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction
Jun 4, 2026Jaeyeong Lee, Wonmo Koo, Heeyoung KimNeural Ordinary Differential Equations Bayesian Neural Networks
Scalable Derivative Gaussian Processes via Exact Gradient Reduction
Jun 1, 2026Hyunseok Seung, Matthias KatzfussGaussian Process Gradient Descent
DisFlow: Scene Flow from Distance Field for Object Pose, Velocity Tracking, and Dynamic Object Reconstruction
Jun 1, 2026Lan Wu, Sheila Sutjipto, Jennifer Wakulicz +1Optical Flow Surface Reconstruction
On the Uncertainty Quantification Ability of Tabular Foundation Models
May 31, 2026Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2Tabular Prior-Data Fitted Network Reliable Uncertainty Quantification
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 TandohMalaria Environmental Uncertainty
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Many-Body Physics Gaussian Process
Scalable Bayesian Inference for Nonlinear Conservation Laws
May 29, 2026Tim Weiland, Philipp HennigReliable Uncertainty Quantification Ill-Posed Inverse Problem
Spectral Anatomy of Quantum Gaussian Process Kernels
May 29, 2026Jian Xu, Chao Li, Guang Lin +4Quantum Kernels Gaussian Process
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
May 28, 2026Thalea Schlender, Peter A. N. Bosman, Tanja AlderliestenSurvival Analysis Decision Trees
Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
May 27, 2026Julien Lalanne, David Picard, Lionel Boillot +3Generative Flow Networks Generative Models
Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS
May 26, 2026Ujjwala VadrevuConditional-Value-At-Risk Stress Testing
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
May 26, 2026Siddharth Chaini, Federica B. Bianco, Ashish MahabalPhase-Folded Light Curves Gaussian Process
Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
May 26, 2026Kane Warrior, Dalia ChakrabartyGaussian Process Covariance Estimation
Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes
May 26, 2026Eichi UeharaHeterogeneous Treatment Effects Causal Inferences
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianGaussian Process Multi-Gpu Systems
Collaborative Navigation and Exploration with $β$ -Sparse Gaussian Processes
May 25, 2026Evangelos Psomiadis, Dipankar Maity, Panagiotis TsiotrasSocial Robot Navigation Simultaneous Localization And Mapping
Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
May 25, 2026Maria Bånkestad, Sanna Jarl, Jens SjölundGaussian Process Posterior
Onsager-Machlup Posterior Transport for Deep Gaussian Processes
May 22, 2026Jian Xu, Delu Zeng, John Paisley +1Gaussian Process Posterior
Regret-Based $(ε,δ)$ -optimal Stopping Criteria for Bayesian Optimization
May 21, 2026Haowei Wang, Jingyi Wang, Qiyu WeiBayesian Optimization Optimal Stopping
Aerodynamic force reconstruction using physics-informed Gaussian processes
May 21, 2026Gledson Rodrigo Tondo, Igor Kavrakov, Guido MorgenthalAerodynamics Gaussian Process
Three Costs of Amortizing Gaussian Process Inference with Neural Processes
May 20, 2026Robin YoungGaussian Process Truncation
Conditioning Gaussian Processes on Almost Anything
May 20, 2026Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite +5Gaussian Process 3D Gaussian Splatting
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
May 20, 2026Mehmet Yigit Balik, Harri LähdesmäkiSingle-Cell Rna Sequencing Gaussian Process
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
May 19, 2026Aurélien Pion, Emmanuel VazquezBayesian Optimization Gaussian Process
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
May 19, 2026Kieran Wood, Stefan Zohren, Stephen J. RobertsDistribution Shifts Gaussian Process
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
May 18, 2026Taeyoung Yun, Woocheol Shin, Inhyuck Song +2Bayesian Optimization Gaussian Process
Lightweight Gaussian Process Inference in C++ on Metal and CUDA
May 18, 2026Yu-Hsueh FangGaussian Process Cuda
A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices
May 17, 2026Ujjwala VadrevuStochastic Volatility Conditional-Value-At-Risk
Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
May 13, 2026Jannis Lübsen, Annika EichlerBayesian Optimization Gaussian Process
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberGaussian Process Periodic Domains
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Ordinary Differential Equations Gaussian Process
Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
May 12, 2026Kumbha Nagaswetha, Rabi PathakBayesian Optimization Bayesian
Post-ADC Inference: Valid Inference After Active Data Collection
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Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
May 11, 2026Kane Warrior, Dalia ChakrabartyGaussian Process Anisotropy
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
May 11, 2026Jixiang Qing, Henry Moss, Matthias SachsGaussian Process Active Learning
Online Sharp-Calibrated Bayesian Optimization
May 11, 2026Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1Bayesian Optimization Gaussian Process
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantGaussian Process Classical Numerical Solver
Scalable Gaussian process inference via neural feature maps
May 11, 2026Anthony StephensonGaussian Process Kernel Method
Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning
May 10, 2026Stefano Tonini, Soroush Rastegarpour, Hamid Reza Feyzmahdavian +2Lyapunov Function Gaussian Process
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
May 9, 2026Connor Holmes, Sven Lilge, Zi Cong Guo +2Factor Graphs State Estimation
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 +1Gaussian Process Computational Chemistry
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May 8, 2026Lucas Nerone Rillo, Zhanhong Jiang, Nastaran Saadati +4Black-Box Optimization Gaussian Process
Implicit Multi-Camera System Calibration Using Gaussian Processes
May 8, 2026Ivan De Boi, Bart Ribbens, Veronika Golanova +2Camera Intrinsics Gaussian Process
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
May 7, 2026Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2Gaussian Process Lagrangian Methods
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
May 7, 2026Joseph D. RamseyCausal Foundation Models Conditional Independence
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
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Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
May 6, 2026Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6Bayesian Optimization Chemical Process Systems
Transformed Latent Variable Multi-Output Gaussian Processes
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Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications
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Deep Kernel Learning for Stratifying Glaucoma Trajectories
May 1, 2026Bruce Rushing, Angela Danquah, Alireza Namazi +2Patient Trajectories Interpretable Diabetic Retinopathy Grading
Bayesian Optimization in Linear Time
Apr 30, 2026Jesse Schneider, William J. WelchBayesian Optimization Gaussian Process
Provable and scalable quantum Gaussian processes for quantum learning
Apr 30, 2026Jonas Jäger, Paolo Braccia, Pablo Bermejo +3Quantum Machine Learning Gaussian Process
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
Apr 30, 2026Daniel Waxman, Fernando Llorente, Petar M. DjurićGaussian Process Signal Processing
Bayesian policy gradient and actor-critic algorithms
Apr 30, 2026Mohammad Ghavamzadeh, Yaakov Engel, Michal ValkoPolicy Gradient Soft Actor-Critic
Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
Apr 30, 2026Minghao Gu, Weizhi Lin, Qiang HuangGaussian Process Covariate Shift
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Apr 29, 2026Albert Saiapin, Kim BatselierBayesian Neural Networks Gaussian Process
PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
Apr 29, 2026Marcus Haywood-Alexander, Gregory Duthé, Eleni ChatziState Estimation Extended Kalman Filter
Rethinking Trust Region Bayesian Optimization in High Dimensions
Apr 24, 2026Wei-Ting Tang, Joel A. PaulsonBayesian Optimization Trust Region

Jun 5, 2026 · cs.LGJ/K move · Enter open · S save

Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts

Alma Rahat, Tinkle Chugh, Jonathan Fieldsend, Richard Allmendinger

Loughborough University, Loughborough, LE11 3TU, U.K. · University of Exeter, Exeter EX4 4QD, U.K. · The University of Manchester, Manchester M15 6PB, U.K.

This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rather than replacing existing Pareto-compliant acquisition functions, the proposed approach augments them with local stationarity information derived from surrogate-derived gradients, enabling faster convergence toward the global Pareto set under limited evaluation budgets. Two catalyst instantiations are investigated: an adaptive Multiple-Gradient Descent Algorithm-Based Catalyst (MGDA) and a predefined-weight variant that enables focused exploration when budgets are tight. Experiments on the DTLZ benchmark suite (using 2 objectives and 10 decision variables) show that predictive gradient catalysis can deliver significant acceleration compared to other acquisition functions (EHVI, AugTch, tMPoI, SAF) when surrogates are accurate, particularly for stationary problems.

Bayesian Optimization Multi-Objective Optimization Time-Step-Wise Learnable Surrogate Gradient Function Gaussian Process Heterogeneous Catalysis

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