Bayesian Optimization
Showing 22 of 22 projects
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
A Python library for distributed asynchronous hyperparameter optimization over complex search spaces.
A complete AI-driven process using GANs with LSTM and CNN to predict stock price movements, incorporating diverse data sources and hyperparameter optimization.
A modular library for Bayesian optimization built on PyTorch, enabling efficient optimization of expensive black-box functions.
An easy-to-use, scalable hyperparameter optimization framework for Keras models with define-by-run syntax and built-in search algorithms.
An accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments using Bayesian and bandit optimization.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A web-based tool for automated hyperparameter tuning and stacked ensemble creation in Python.
A versatile Bayesian optimization package for hyperparameter optimization of machine learning algorithms.
A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.
A decentralized hyperparameter optimization framework for Go, inspired by Optuna, supporting Bayesian optimization and evolution strategies.
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
A Python toolkit for optimizing chemical reactions using machine learning strategies and benchmarks.
A lightweight Bayesian optimization library built on JAX for efficient optimization of expensive-to-evaluate functions.
A simple yet essential Python framework for Bayesian optimization, enabling efficient hyperparameter tuning and black-box function optimization.
A Python library providing SigOpt hyperparameter optimization wrappers for scikit-learn and XGBoost models.
Provides SigOpt wrappers for scikit-learn to optimize hyperparameters and facilitate model selection.
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