Apr 8, 2024 · In this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR.

Abstract— Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications.

With ADVGP, we effortlessly scale up GP regression to a real-world applica- tion with billions of samples and demonstrate an excellent, superior prediction ...

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications.

Abstract: A Gaussian process regression (GPR) can be used as a stochastic method for modeling underwater terrain using multibeam sonar data.

Parallel BO methods often adopt single manager/multiple workers strategies to evaluate multiple hyperparameter configurations simultaneously. Despite.

We describe the mean function of the Gaussian process by approximating marginals of a Markov random field (MRF). Variability around the mean is modeled with a ...

A Data Parallel Approach for Large-Scale Gaussian Process Modeling · Local and global sparse Gaussian process approximations · Sparse Gaussian Processes using ...

Banerjee, Dunson, and Tokdar proposed a GP regression algorithm for large datasets using Random Projection Method and Nyström approximation. However, they have ...

Jul 12, 2021 · 2.1 Bayesian optimization with Gaussian processes. Bayesian Optimization is a sequential strategy for optimizing black-box objective functions, ...