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

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

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

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

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

Interactive Gaussian Processes

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

Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems

Incremental Sparse Spectrum Gaussian Process Regression

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