Gaussian processes in TensorFlow

Bayesian Optimization using GPflow

Non-stationary spectral mixture kernels implemented in GPflow

🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0

Gaussian-Processes Surrogate Optimisation in python

Library for Deep Gaussian Processes based on GPflow

📈 Implementation of the Graph Gaussian Process using GPflow and TensorFlow 2

Actually Sparse Variational Gaussian Processes implemented in GPlow

Methods for estimating time-varying functional connectivity (TVFC)

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

Sparse Heteroscedastic Gaussian Processes

Subset of Data Variational Inference for Deep Gaussian Process Model

Mode-constrained model-based-reinforcement learning in TensorFlow/GPflow

Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.

Gaussian processes in TensorFlow

Implements AT-GP from Cao et. al. 2010 in GPflow