Gaussian Process
Description
Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.
Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams
Papers Using This Method
Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics2025-07-15A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification2025-07-15Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics2025-06-26Active Learning for Manifold Gaussian Process Regression2025-06-26Trustworthy Prediction with Gaussian Process Knowledge Scores2025-06-23Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction2025-06-20Single-Example Learning in a Mixture of GPDMs with Latent Geometries2025-06-17Digital twin for virtual sensing of ferry quays via a Gaussian Process Latent Force Model2025-06-17Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy2025-06-16Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification2025-06-16Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes2025-06-15Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?2025-06-13RoCA: Robust Cross-Domain End-to-End Autonomous Driving2025-06-11Not all those who drift are lost: Drift correction and calibration scheduling for the IoT2025-06-10Data-Driven Nonlinear Regulation: Gaussian Process Learning2025-06-10Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment2025-06-10Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control2025-06-06Gaussian Process Diffeomorphic Statistical Shape Modelling Outperforms Angle-Based Methods for Assessment of Hip Dysplasia2025-06-05Nonlinear Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation with Hardware Impairments2025-06-04A Data-Based Architecture for Flight Test without Test Points2025-06-02