Sigma-Point Filters based on Bayesian Quadrature

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

Resources and extra documentation for the manuscript "A Global Sensitivity-based Identification of Key Factors on Stability of Power Grid with Multi-outfeed HVDC" published in IEEE Latin America Transactions.

Treed Gaussian process algorithm in Python

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

Data and code associated with paper "On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions" currently in review.

My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.

Hyper-Parameter Tuning / BayesianOptimization / Gaussian Process / etc.

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

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

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

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

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

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization