SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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Updated
Jul 23, 2024 - Python
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
Flexible Bayesian Optimization in R
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
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.
Automated Bayesian model discovery for time series data
The Docker container for MGPfact is primarily used for unsupervised manifold learning of single-cell RNA-seq data and can factorize complex cell trajectories into interpretable branching Gaussian processes.
My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.
Bayesian Learning for Control in Multimodal Dynamical Systems | written in Org-mode
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.
Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
Treed Gaussian process algorithm in Python
A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization
A minimal implementation of Gaussian process regression in PyTorch
Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization
Gaussian Process localization with ToF and RSSI
Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms
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