SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

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

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

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

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

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

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

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.

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

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

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

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.

Sigma-Point Filters based on Bayesian Quadrature

Advanced Active Learning platform for engineering simulations. Reduces simulation costs by through intelligent surrogate modeling and physics-informed sampling.

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