awesome-AutoML

(Tang et al. 2021)

(Santu et al. 2020)

(Ren et al. 2020)

(Yang et al. 2020)

(Zoller et al. 2019)

(He et al. 2019)

(Wistuba et al. 2019)

(Elsken et al. 2019)

(Yao et al. 2018)

(Saad-Falcon. 2024)

(Fan et al. 2023)

(Chen et al. 2023)

(Jin et al. 2022)

(Liu et al. 2022)

(Akin et al. 2022)

(Yang et al. 2022)

(Li et al. 2022)

(Zhou et al. 2022)

(Speckhard et al. 2022)

(Xu et al. 2021)

(So et al. 2021)

(Xu et al. 2021)

(Li et al. 2021)

(Wang et al. 2021)

(Wang et al. 2021)

(Ru et al. 2021)

(Chen et al. 2021)

(Lin et al. 2021)

(Lin et al. 2021)

(Li et al. 2021)

(Zhu et al. 2021)

(Zhou et al. 2021)

(Xu et al. 2021)

(Li et al. 2021)

(Lin et al. 2021)

(Peng et al. 2021)

(Chu et al. 2021)

(Chen et al. 2020)

(Wang et al. 2020)

(Xu et al. 2020)

(Zhao et al. 2020)

(Yao et al. 2020)

(Peng et al. 2020)

(Zhang et al. 2020)

(Xu et al. 2020)

(Wang et al. 2020)

(Lin et al. 2020)

(Wan et al. 2020)

(Xiong et al. 2020)

(Lu et al. 2020)

(Wang et al. 2020)

(Guo et al. 2020)

(Luo et al. 2020)

(Chu et al. 2020)

(Real et al. 2020)

(Such et al. 2019)

(Yang et al. 2019)

(Elsken et al. 2019)

(Kasim et al. 2019)

(Hue et al. 2019)

(Kang et al. 2019)

(Li et al. 2019)

(Ghiasi et al. 2019)

(Mazzawi et al. 2019)

(Du et al. 2019)

(Yao et al. 2019)

(Sciuto et al. 2019)

(Tan et al. 2019)

(Zheng et al. 2019)

(Ma et al. 2019)

(Xu et al. 2019) -

(Guo et al. 2019)

(Gong et al. 2019)

(Tan et al. 2019)

(Piergiovanni et al. 2019)

(Ryoo et al. 2019)

(Gupta et al. 2019)

(Tan et al. 2019)

(Chu et al. 2019) -

(Howard et al. 2019)

(Liu et al. 2019)

(Chen et al. 2019)

(Zhang et al. 2019)

(Zheng et al. 2019)

(Chu et al. 2019)

(Fedorov et al. 2019)

(Zhu et al. 2019)

(Guo et al. 2019)

(Cheng et al. 2019)

(Cai et al. 2019)

(Ying et al. 2019)

(Liang et al. 2019)

(Chu et al. 2019)

(So et al. 2019)

(Xie et al. 2019)

(Sood et al. 2019)

(Fang et al. 2019)

(Bender et al. 2018)

(Piergiovanni et al. 2018)

(Guo et al. 2018)

(Kandasamy et al. 2018)

(Cai et al. 2018)

(Zhong et al. 2018)

(Véniat et al. 2018)

(Nayebi et al. 2018)

(Luo et al. 2018)

(Tan et al. 2018)

(Elsken et al. 2018)

(Hsu et al. 2018)

(Yang et al. 2018)

(Kim et al. 2018)

(Gordon et al. 2018)

(Dong et al. 2018)

(Cheng et al. 2018)

(Liu et al. 2018)

(Real et al. 2018)

(Cai et al. 2017)

(Real et al. 2017)

(Liu et al. 2017)

(Cortes et al. 2017)

(Zoph et al. 2017)

(Xu et al 2020)

(Garg et al 2020)

(Ying et al. 2019) -

(Chen et al. 2017)

(Bello et al. 2017)

(Ramachandran et al. 2017)

(Zhou et al. 2020)

(Park et al. 2019)

(Cubuk et al. 2019)

(Zoph et al. 2019)

(Lim et al. 2019)

(Cubuk et al. 2018)

(Pham et al. 2020)

(Zhang et al. 2022)

(Song et al. 2019)

(Chelsea Finn PhD disseration 2018)

(OpenAI Reptile by Nichol et al. 2018)

(MAML by Finn et al. 2017)

(Mishra et al. 2017)

(Chen et al. 2016)

(Andrychowicz et al. 2016)

(Wang et al. 2016)

(Duan et al. 2016)

(Chen et al. 2022)

(Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021)

(Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021)

(Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021)

(Metz et al. 2020)

(Tu et al. 2019)

(Jaderberg et al. 2017)

(Golovin et al. 2017)

(Li et al. 2016)

(Snoek et al. 2012)

(Bergstra et al. 2012)

(Kanter et al. 2017)

(Katz et al. 2016)

(Wen et al. 2023)

(Zheng et al. 2022)

(Chen et al. 2022)

(Zhao et al. 2021)

(Zhao et al. 2021)

(Liu et al. 2021)

(Zhao et al. 2021)

(Zhu et al. 2021)

(Song et al. 2020)

(Liu et al. 2020)

(Khawar et al. 2020)

(Liu et al. 2020)

(Joglekar et al. 2019)

(He et al. 2018)

(Wang et al. 2018)

(Luo et al. 2021)

(Dutta et al. 2019)

(Parker-Holder et al. 2022)

(Baker et al. 2016)

(Zoph and Le. 2016)

(Guan et al. 2021)

(Li et al. 2020)

(Wang et al. 2022)

(Zhang et al. 2020)

(Zhou et al. 2022)

(Zhang et al. 2022)

(Shin et al. 2020)

(Sarah et al. 2024)

(Deci Research 2023)

Falcon

: A Lightweight AutoML Library

MindWare

: Efficient Open-source AutoML System

AutoDL

: automated deep learning

AutoGL

: An autoML framework & toolkit for machine learning on graphs

MLBox

: a powerful Automated Machine Learning python library

FLAML

: Fast and lightweight AutoML ( )

Hypernets

: A General Automated Machine Learning Framework

Cooka

: a lightweight and visualization toolkit

Vegas

: an AutoML algorithm tool chain by Huawei Noah's Arb Lab

TransmogrifAI

: an AutoML library written in Scala that runs on top of Apache Spark

Model Search

: a framework that implements AutoML algorithms for model architecture search at scale

: AutoML Toolkit for Deep Learning

hyperunity

: A toolset for black-box hyperparameter optimisation

auptimizer

: An automatic ML model optimization tool

Keras Tuner

: Hyperparameter tuning for humans

Torchmeta

: A Meta-Learning library for PyTorch

learn2learn

: PyTorch Meta-learning Framework for Researchers

Auto-PyTorch

: Automatic architecture search and hyperparameter optimization for PyTorch

: A multi-tenant, multi-data system for automated machine learning (model selection and tuning)

Adanet: Fast and flexible AutoML with learning guarantees

: Tensorflow package for AdaNet

Microsoft Neural Network Intelligence (NNI)

: An open source AutoML toolkit for neural architecture search and hyper-parameter tuning

Dragonfly

: An open source python library for scalable Bayesian optimisation

: Automatic Machine Learning by H2O.ai

Kubernetes Katib

: hyperparameter Tuning on Kubernetes inspired by Google Vizier

: Scalable Hyperparameter Tuning¶

: automated machine learning for structured data by Salesforce

Advisor

: open-source implementation of Google Vizier for hyper parameters tuning

: AutoML library by Texas A&M University using Bayesian optimization

: an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator

Ludwig

: a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code

: hyperparameter search for Weka

automl-gs

: Provide an input CSV and a target field to predict, generate a model + code to run it

SMAC

: Sequential Model-based Algorithm Configuration

Hyperopt-sklearn

: hyper-parameter optimization for sklearn

Spearmint

: a software package to perform Bayesian optimization

: one of the very first AutoML methods and open-source software packages

MOE

: a global, black box optimization engine for real world metric optimization by Yelp

Hyperband

: open source code for tuning hyperparams with Hyperband

: define-by-run hypterparameter optimization framework

RoBO

: a Robust Bayesian Optimization framework

HpBandSter

: a framework for distributed hyperparameter optimization

HPOlib2

: a library for hyperparameter optimization and black box optimization benchmarks

: distributed Asynchronous Hyperparameter Optimization in Python

REMBO

: Bayesian optimization in high-dimensions via random embedding

: a framework for automated feature generation

FeatureTools

: An open source python framework for automated feature engineering

EvalML

: An open source python library for AutoML

PocketFlow

: use AutoML to do model compression (open sourced by Tencent)

DEvol (DeepEvolution)

: a basic proof of concept for genetic architecture search in Keras

mljar-supervised

: AutoML with explanations and markdown reports

Determined

: scalable deep learning training platform with integrated hyperparameter tuning support; includes Hyperband, PBT, and other search methods

AutoGL

: an autoML framework & toolkit for machine learning on graphs)

FEDOT

: AutoML framework for the design of composite pipelines

NASGym

: a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS)

Archai

: a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications

autoBOT

: An autoML system for automated text classification exploiting representation evolution

autoai

: A framework to find the best performing AI/ML model for any AI problem

: Automated Neural Architecture Construction (AutoNAC™)

: Augment experts. Empower citizen data scientists

: Effortlessly Embed Cutting-Edge AI Into Your Apps

Syne Tune

: state-of-the-art distributed hyperparameter optimizers (HPO)

[EfficientML.ai Lecture 8 - Neural Architecture Search (Part II) (MIT 6.5940, Fall 2023)( )

by Evolving AI Lab

by Frank Hutter and Joaquin Vanschoren

by Debadeepta Dey (MSR)

by Quoc Le (Google Brain)

by Roboflow

-

A book that introduces autoML with AutoKreas and Keras Tuner

by Waymo