Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments

Hyperpatameter Bayesian Optimization for Image Classification in PyTorch

Bayesian Optimization for MPPI Control of Robot Arm Planar Pushing

Dataset and code for "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning"

We have created a module to run the Gaussian process model. We have implemented the code based on GPyTorch.

Uncertainty in convolutional neural network predictions using Gaussian processes

A Fast and Simplified Python Library for Uncertainty Estimation

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

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

Implementation of Cyclist Pressure Research Paper

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

Testing deployment of a Ray cluster on AWS

Explore selected topics related to Gaussian processes