GP Regression

Gaussian process (GP) regression is a powerful machine learning technique used to model and predict relationships between variables, offering probabilistic uncertainty estimates alongside predictions. Current research focuses on improving GP scalability for large datasets through methods like sparse approximations, state-space representations, and nearest-neighbor approaches, as well as enhancing model expressiveness by incorporating gradient information and addressing limitations of stationary kernels. These advancements are enabling the application of GP regression to increasingly complex problems across diverse fields, including multifidelity modeling, time series analysis (e.g., solar power forecasting), and Bayesian optimization, ultimately improving the accuracy and efficiency of predictive modeling.

Papers

August 4, 2024

Efficient Decision Trees for Tensor Regressions
Hengrui Luo, Akira Horiguchi, Li Ma
Decision Tree Tensor Network Tensor Regression GP Regression Tensor on Tensor Regression

June 29, 2024

Markovian Gaussian Process: A Universal State-Space Representation for Stationary Temporal Gaussian Process
Weihan Li, Yule Wang, Chengrui Li, Anqi Wu
Gaussian Process Stationary Kernel Gauss Markov State Space Representation GP Regression

February 25, 2024

Gradient-enhanced deep Gaussian processes for multifidelity modelling
Viv Bone, Chris van der Heide, Kieran Mackle, Ingo H. J. Jahn, Peter M. Dower, Chris Manzie
Gaussian Process Gradient Information Deep Gaussian Process GP Regression Multifidelity Linear Regression

October 4, 2023

Stationarity without mean reversion in improper Gaussian processes
Luca Ambrogioni
Gaussian Process Positive Definite Kernel Stationary Kernel Covariance Function GP Regression

July 10, 2023

Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
Armin Lederer, Jonas Umlauft, Sandra Hirche
Gaussian Process Learning Based Gaussian Process Regression Real Time Tracking GP Regression

June 26, 2023

Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
Robert Allison, Anthony Stephenson, Samuel F, Edward Pyzer-Knapp
Native Robustness Gaussian Process Uncertainty Calibration GP Regression

June 6, 2023

Memory-Based Dual Gaussian Processes for Sequential Learning
Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Continual LEArning Gaussian Process Bayesian Optimization Sequential Learning GP Regression

April 11, 2023

Actually Sparse Variational Gaussian Processes
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth
Gaussian Process B Spline Sparse Matrix Sparse Variational Gaussian Process GP Regression

February 1, 2023

Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
Sean Nassimiha, Peter Dudfield, Jack Kelly, Marc Peter Deisenroth, So Takao
Gaussian Process Online Filtering Solar Power Generation Data Short Term Prediction Gaussian Process State Space Model GP Regression GP Inference

November 22, 2021

Transfer Learning with Gaussian Processes for Bayesian Optimization
Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska
Transfer Learning Gaussian Process Bayesian Optimization Bayesian Transfer GP Regression