PyXAB - A Python Library for $\mathcal{X}$-Armed Bandit and Online Blackbox Optimization Algorithms
PyXAB - A Python Library for $\mathcal{X}$-Armed Bandit and Online Blackbox Optimization Algorithms - Published in JOSS (2024)
smac
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
metasklearn
MetaSklearn: A Metaheuristic-Powered Hyperparameter Optimization Framework for Scikit-Learn Models.
https://github.com/polyaxon/hypertune
A library for performing hyperparameter optimization
SDMtune
Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.
https://github.com/awslabs/renate
Library for automatic retraining and continual learning
https://github.com/grailbio/diviner
Diviner is a serverless machine learning and hyper parameter tuning platform
https://github.com/bbopt/hypernomad
A library for the hyperparameter optimization of deep neural networks
drugresponseeval
Pipeline for testing drug response prediction models in a statistically and biologically sound way.
https://github.com/ai-team-uoa/autoer
Code & Experiments for IEEE Access paper "Auto-Configuring Entity Resolution Pipelines" by K.Nikoletos, V.Efthymiou, G.Papadakis and K.Stafanidis
dpl
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
mlp_hpp_analysis
This repository is the code basis for the paper intitled "Exploring the Intricacies of Neural Network Optimization"
https://github.com/awslabs/adatune
Gradient based Hyperparameter Tuning library in PyTorch
syne-tune
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
rapidx
A toolbox that integrates with PyTorch-Lightning which helps in running and managing multiple codebases
https://github.com/copyleftdev/tundr
A high-performance optimization server implementing the Model Context Protocol (MCP) for mathematical optimization tasks, with a focus on Bayesian Optimization using Gaussian Processes. Designed for reliability, scalability, and ease of integration in production environments.
propulate
Propulate is an asynchronous population-based optimization algorithm and software package for global optimization and hyperparameter search on high-performance computers.
https://github.com/cmower/hyparam
Container for hyper parameter tuning in machine learning.
agilerl
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.