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

Uncertainty in convolutional neural network predictions using Gaussian processes

Proof-of-principle application of Gaussian process modeling to gamma-ray analyses. Code repository associated with the paper https://arxiv.org/abs/2010.10450.

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

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

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

A Fast and Simplified Python Library for Uncertainty Estimation

Bayesian Optimization for MPPI Control of Robot Arm Planar Pushing

Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.

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

Models for EthicML

Explores the application of Gaussian Process (GP) and sparse GP algorithms to handle missing heart rate time series dataset. Our findings emphasize the importance of kernel selection, specifically the RBF kernel, and the careful tuning of hyperparameters to achieve optimal performance in imputation tasks

Implementation of Gaussian Process (GP) models using GPyTorch.

Implementation of Cyclist Pressure Research Paper

Contains code for Adaptive protection platform in Smart grids

Evaluating Deep Gaussian processes

Explore selected topics related to Gaussian processes

Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.