SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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Updated
Oct 24, 2024 - Python
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
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.
Hyper-Parameter Tuning / BayesianOptimization / Gaussian Process / etc.
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
My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.
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