Multi-Output Gaussian Processes

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11 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization
Sep 26, 2026Zikai XieMulti-Output Gaussian Processes Bayesian Optimization
State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei, Anhui 230000, China
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
Multi-output Gaussian process prediction of physical fields under linear equality constraints
Aug 26, 2026Mahamat Hamdan Nassouradine, Clément Gauchy, Pierre-Emmanuel Angeli +1Multi-Output Gaussian Processes Surrogate Modeling
Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation, Gif-sur-Yvette, 91191, France · Univ Rennes, Ensai, CNRS, CREST - UMR 9194, Rennes, F-35000, France · Université Paris-Saclay, CEA, Service de Thermohydraulique et de Mécanique des Fluides, Gif-sur-Yvette, 91191, France
A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
Aug 12, 2026Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko +2Multi-Output Gaussian Processes Gaussian 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
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Jun 4, 2026Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2Latent Variable Models Climate Emulation
University of Cambridge · University of Colorado Boulder · University of Oxford +1
Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
May 13, 2026Jannis Lübsen, Annika EichlerBayesian Optimization Gaussian Processes
Institute of Control Systems, Hamburg University of Technology, Hamburg, Germany · Institute of Control Systems, Hamburg University of Technology · Deutsches Elektronen-Synchrotron, Hamburg, Germany
Transformed Latent Variable Multi-Output Gaussian Processes
May 6, 2026Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2Deep Kernel Learning Latent Variable Models
Department of Computer Science, University of Manchester, Manchester, UK · Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Consensus-based Recursive Multi-Output Gaussian Process
Apr 11, 2026Yogesh Prasanna Kumar Rao, Tamas Keviczky, Raj Thilak RajanMulti-Output Gaussian Processes Gaussian Processes
Improving Forecasts of Suicide Attempts for Patients with Little Data
Nov 22, 2025Genesis Hang, Annie Chen, Hope Neveux +2Latent Variable Models Gaussian Processes
Wellesley College · Harvard University
Spectral structure learning for clinical time series
Feb 17, 2025Ivan Lerner, Anita Burgun, Francis BachMulti-Output Gaussian Processes Time-Series Causal Discovery
Université Paris Cité, Inria, Inserm, HeKA, F-75015 Paris, France · Service d’informatique biomédicale, AP-HP, Hôpital Georges Pompidou, F-75015 Paris, France · SIERRA, Inria Paris, F-75015 Paris, France

Sep 26, 2026J/K move · Enter open · S save

Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization

Zikai Xie

State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei, Anhui 230000, China

Multi-Output Gaussian Processes Bayesian Optimization Multi-Objective Optimization Gaussian Processes Gaussian Process Regression

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Multi-objective Bayesian optimization (MOBO) commonly relies on independent Gaussian processes (GPs) with stationary kernels, limiting its ability to represent nonstationary structure and share information between objectives. However, expressive nonstationary GPs do not necessarily make reliable BO decisions. We study this mismatch for the multi-output low-rank nonstationary (MO-LRN) GP: strong training fit can coexist with large off-design errors and optimistic acquisition predictions. We introduce MOLRN-BO, which combines a regularized shared-spectral surrogate with objective-specific residuals, prequential mean correction and tempered covariance scaling, and Pareto-local qLogEHVI optimization with periodic global search. Experiments on 12 deterministic bi-objective benchmarks show that MOLRN-BO substantially improves upon the original MO-LRN and achieves the best average problem ranks for final normalized hypervolume and normalized inverted generational distance among nine evaluated algorithms. It also achieves the strongest adverse-tail performance while remaining competitive with the leading baselines in anytime optimization. Ablation studies further show that the shared spectral construction improves off-design prediction, the local--global decision policy improves optimization performance, and hierarchical calibration reduces systematic candidate bias. These results demonstrate that nonstationary multi-output surrogates can deliver strong and robust MOBO performance when their structure and use are explicitly adapted to the demands of sequential optimization.