Top 63 bayesian-optimization open source projects

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

Bayesian Optimization using GPflow

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Anomaly detection for temporal data using LSTMs

Sequential model-based optimization with a `scipy.optimize` interface

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Decentralized hyperparameter optimization framework, inspired by Optuna.

Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB

A lightweight framework for Gaussian processes and Bayesian optimization of black-box functions (C++-11)

Surrogate Optimization Toolbox for Python

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

A toolset for black-box hyperparameter optimisation.

BOPP: Bayesian Optimization for Probabilistic Programs

A fully decentralized hyperparameter optimization framework

GPstuff - Gaussian process models for Bayesian analysis

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Notebooks about Bayesian methods for machine learning

A modular active learning framework for Python

Bayesian Optimization of Combinatorial Structures

Simple, but essential Bayesian optimization package

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Automated Machine Learning with scikit-learn

Sequential Model-based Algorithm Configuration

A Python implementation of global optimization with gaussian processes.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

a distributed Hyperband implementation on Steroids

Experimental Global Optimization Algorithm

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

ytopt: machine-learning-based search methods for autotuning

The standard and parallel multiobjective EGO algorithms

Sequential Gallery for Interactive Visual Design Optimization [SIGGRAPH 2020]

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

AutoOED: Automated Optimal Experimental Design Platform

Phoenics: Bayesian optimization for efficient experiment planning

Generalized and Efficient Blackbox Optimization System [SIGKDD'21].

Implementation of Stochastic Gradient MCMC algorithms

Bayesian Optimization in JAX

Yes, it's a camel case.

Python library for Bayesian hyper-parameters optimization

Bayesian Optimization and Grid Search for xgboost/lightgbm

Hyperparameter optimization in Julia.

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Fitness landscape exploration sandbox for biological sequence design.

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Gaussian processes and Bayesian optimization for images and hyperspectral data

Deodorant: Solving the problems of Bayesian Optimization

Using Bayesian Optimization to optimize hyper parameter in Keras-made neural network model.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Tutorial on Bayesian optimization in R

Learnable Embedding Space for Efficient Neural Architecture Compression

PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Bayesian Optimization algorithms with various recent improvements

Mixed-Integer Parallel Efficient Global Optimization

Codebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.

Parallel Hyperparameter Tuning in Python

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.