SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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Updated
Jun 25, 2024 - Python
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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.
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.
1D, super-resolution brightness profile reconstruction for interferometric sources
sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model
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
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.
A NumPy implementation of Lee et al., Deep Neural Networks as Gaussian Processes, 2018
Hyper-Parameter Tuning / BayesianOptimization / Gaussian Process / etc.
Multi-output Gaussian process regression via multi-task neural network
Sigma-Point Filters based on Bayesian Quadrature
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