SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model

{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

Automated Bayesian model discovery for time series data

A minimal implementation of Gaussian process regression in PyTorch

Flexible Bayesian Optimization in R

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

1D, super-resolution brightness profile reconstruction for interferometric sources

A NumPy implementation of Lee et al., Deep Neural Networks as Gaussian Processes, 2018

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

Multi-output Gaussian process regression via multi-task neural network

Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms

Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)

Interactive Gaussian Processes

Incremental Sparse Spectrum Gaussian Process Regression

A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization

Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.

Gaussian-Process Surrogate Optimisation