gaussian-process

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

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

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

Automated Bayesian model discovery for time series data

A minimal implementation of Gaussian process regression in PyTorch

Flexible Bayesian Optimization in R

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

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

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

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

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

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

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

Research code and reproducibility materials for coarse- and fine-grained time-series forecasting using Gaussian process blurring and denoising.

Incremental Sparse Spectrum Gaussian Process Regression

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测