bayesian-optimization
There are 704 repositories under bayesian-optimization topic.
microsoft/nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
bayesian-optimization/BayesianOptimization
A Python implementation of global optimization with gaussian processes.
automl/auto-sklearn
Automated Machine Learning with scikit-learn
scikit-optimize/scikit-optimize
Sequential model-based optimization with a `scipy.optimize` interface
modAL-python/modAL
A modular active learning framework for Python
krasserm/bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
google/vizier
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
SimonBlanke/Gradient-Free-Optimizers
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
automl/SMAC3
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
MIND-Lab/OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
EmuKit/emukit
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
automl/HpBandSter
a distributed Hyperband implementation on Steroids
SimonBlanke/Hyperactive
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
chrisstroemel/Simple
Experimental Global Optimization Algorithm
ray-project/tune-sklearn
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
PKU-DAIR/open-box
Towards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)
ARM-software/mango
Parallel Hyperparameter Tuning in Python
syne-tune/syne-tune
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
emdgroup/baybe
Bayesian Optimization and Design of Experiments
sherpa-ai/sherpa
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
experimental-design/bofire
Experimental design and (multi-objective) bayesian optimization.
rentruewang/bocoel
Bayesian Optimization as a Coverage Tool for Evaluating LLMs. Accurate evaluation (benchmarking) that's 10 times faster with just a few lines of modular code.
GPflow/GPflowOpt
Bayesian Optimization using GPflow
c-bata/goptuna
A hyperparameter optimization framework, inspired by Optuna.
wujian16/Cornell-MOE
A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
acerbilab/bads
Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB
resibots/limbo
A lightweight framework for Gaussian processes and Bayesian optimization of black-box functions (C++11)
ziatdinovmax/gpax
Gaussian Processes for Experimental Sciences
akash13singh/lstm_anomaly_thesis
Anomaly detection for temporal data using LSTMs
dme65/pySOT
Surrogate Optimization Toolbox for Python
baggepinnen/Hyperopt.jl
Hyperparameter optimization in Julia.
mlr-org/mlrMBO
Toolbox for Bayesian Optimization and Model-Based Optimization in R
janosh/matbench-discovery
An evaluation framework for machine learning models simulating high-throughput materials discovery.
gpstuff-dev/gpstuff
GPstuff - Gaussian process models for Bayesian analysis