Gaussian Process Regression

Gaussian Process Regression (GPR) is a Bayesian non-parametric method used for regression tasks, aiming to predict a continuous output variable based on input data while providing uncertainty estimates. Current research emphasizes improving GPR's scalability and robustness, focusing on techniques like dividing local Gaussian processes for continual learning, tensor network methods for high-dimensional data, and efficient kernel selection and subsampling strategies. These advancements enhance GPR's applicability across diverse fields, including system identification, time series forecasting, safety-critical control systems, and scientific modeling where accurate uncertainty quantification is crucial.

Papers

January 30, 2023

A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins
Susheel Dharmadhikari, Reid A. Berdanier, Karen A. Thole, Amrita Basak
Bayesian Optimization Gaussian Process Regression Aerodynamic Force Dorsal Fin

January 26, 2023

Gaussian process regression and conditional Karhunen-Lo\'{e}ve models for data assimilation in inverse problems
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
Inverse Problem Data Assimilation Gaussian Process Regression Parameter Estimation Model Inversion PDE Model Conditional Model Heterogeneous Parameter

December 23, 2022

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger
Gaussian Process Regression PDE Solver Galerkin Method Physic Informed Gaussian Process Linear Partial Differential Equation

December 14, 2022

Generative structured normalizing flow Gaussian processes applied to spectroscopic data
Natalie Klein, Nishant Panda, Patrick Gasda, Diane Oyen
Generative Model Gaussian Process Generative Question Gaussian Process Regression Conditional Normalizing Flow Spectroscopic Data Gaussian Flow

December 2, 2022

November 26, 2022

November 25, 2022

A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking
Mengwei Sun, Mike E. Davies, Ian K. Proudler, James R. Hopgood
Gaussian Process Gaussian Process Regression Particle Filter Learned Dynamic Target Tracking Multi Target Tracking

November 21, 2022

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces
Sergei Manzhos, Manabu Ihara
Gaussian Process Regression Kernel Ridge Regression Gaussian Kernel Kernel Extension Computational Chemistry Potential Energy Surface

October 26, 2022

Continuum Robot State Estimation Using Gaussian Process Regression on $SE(3)$
Sven Lilge, Timothy D. Barfoot, Jessica Burgner-Kahrs
Robot Person State Estimation Gaussian Process Regression Continuum Robot

October 18, 2022

Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone
Gaussian Process Regression Deep Gaussian Process

October 16, 2022

Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang
Gaussian Process Bayesian Optimization Gaussian Process Regression Performance Guarantee Online Streaming Online Decision

October 13, 2022

Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $\epsilon$-greedy acquisition function
Rui Zhong, Enzhi Zhang, Masaharu Munetomo
Optimization Purpose Bayesian Optimization Evolutionary Algorithm Gaussian Process Regression Differential Evolution Acquisition Function

October 4, 2022

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Michael K. Cohen, Samuel Daulton, Michael A. Osborne
Gaussian Process Gaussian Process Regression Sparse Kernel Sparse GP Subtree Kernel

September 7, 2022

Non-Gaussian Process Regression
Yaman Kındap, Simon Godsill
Bayesian Inference Gaussian Process Regression Heavy Tailed Posterior Predictive Non Gaussianity

September 1, 2022

B\'ezier Gaussian Processes for Tall and Wide Data
Martin Jørgensen, Michael A. Osborne
Gaussian Process Gaussian Process Regression Predictive Performance Kernel Extension Approximate Inference

August 24, 2022

Fast emulation of density functional theory simulations using approximate Gaussian processes
Steven Stetzler, Michael Grosskopf, Earl Lawrence
Gaussian Process Gaussian Process Regression Particle Density Functional Theory

August 22, 2022

Scale invariant process regression: Towards Bayesian ML with minimal assumptions
Matthias Wieler
Visual Analogue Scale Regularization Model Gaussian Process Regression Modeling Assumption Kernel Regime Kernel Optimization Extrapolation Performance Invariant Smoothing

July 17, 2022

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
Lixue Cheng, Jiace Sun, J. Emiliano Deustua, Vignesh C. Bhethanabotla, Thomas F. Miller
Known Molecule Gaussian Process Regression Electronic Structure C++ Implementation Potential Energy Surface Correlation Potential

Gaussian Process Regression

Papers

A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins

Gaussian process regression and conditional Karhunen-Lo\'{e}ve models for data assimilation in inverse problems

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

Generative structured normalizing flow Gaussian processes applied to spectroscopic data

Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels

Empirical Asset Pricing via Ensemble Gaussian Process Regression

A Particle-based Sparse Gaussian Process Optimizer

Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data

A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces

Continuum Robot State Estimation Using Gaussian Process Regression on $SE(3)$

Locally Smoothed Gaussian Process Regression

Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making

Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $\epsilon$-greedy acquisition function

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels

Non-Gaussian Process Regression

B\'ezier Gaussian Processes for Tall and Wide Data

Fast emulation of density functional theory simulations using approximate Gaussian processes

Scale invariant process regression: Towards Bayesian ML with minimal assumptions

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression