AwesomeAutoMLPapers
AwesomeAutoMLPapers is a curated list of automated machine learning papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field. Thanks to all the people who made contributions to this project. Join us and you are welcome to be a contributor.
What is AutoML?
Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for nonMachine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.
Machine Learning (ML) has achieved considerable successes in recent years and an evergrowing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:
 Preprocess the data,
 Select appropriate features,
 Select an appropriate model family,
 Optimize model hyperparameters,
 Postprocess machine learning models,
 Critically analyze the results obtained.
As the complexity of these tasks is often beyond nonMLexperts, the rapid growth of machine learning applications has created a demand for offtheshelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML. As a new subarea in machine learning, AutoML has got more attention not only in machine learning but also in computer vision, natural language processing and graph computing.
There are no formal definition of AutoML. From the descriptions of most papers，the basic procedure of AutoML can be shown as the following.
AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. Put simply, AutoML can lead to improved performance while saving substantial amounts of time and money, as machine learning experts are both hard to find and expensive. As a result, commercial interest in AutoML has grown dramatically in recent years, and several major tech companies and startup companies are now developing their own AutoML systems. An overview comparison of some of them can be summarized to the following table.
Company 
AutoFE 
HPO 
NAS 
4paradigm 
√ 
√ 
× 
Alibaba 
× 
√ 
× 
Baidu 
× 
× 
√ 
Determined AI 
× 
√ 
√ 
Google 
√ 
√ 
√ 
H2O.ai 
√ 
√ 
× 
Microsoft 
× 
√ 
√ 
RapidMiner 
√ 
√ 
× 
Tencent 
× 
√ 
× 
AwesomeAutoMLPapers includes very uptodate overviews of the breadandbutter techniques we need in AutoML:
 Automated Data Clean (Auto Clean)
 Automated Feature Engineering (Auto FE)
 Hyperparameter Optimization (HPO)
 MetaLearning
 Neural Architecture Search (NAS)
Table of Contents
Papers
Surveys
 2019  AutoML: A Survey of the StateoftheArt  Xin He, et al.  arXiv 
PDF
 2019  Survey on Automated Machine Learning  Marc Zoeller, Marco F. Huber  arXiv 
PDF
 2019  Automated Machine Learning: StateofTheArt and Open Challenges  Radwa Elshawi, et al.  arXiv 
PDF
 2018  Taking Human out of Learning Applications: A Survey on Automated Machine Learning  Quanming Yao, et al.  arXiv 
PDF
 2020  On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice  Li Yang, et al.  Neurocomputing 
PDF
 2020  Automated Machine Learninga brief review at the end of the early years  Escalante, H. J.  arXiv 
PDF
Automated Feature Engineering

Expand Reduce
 2017  AutoLearn — Automated Feature Generation and Selection  Ambika Kaul, et al.  ICDM 
PDF
 2017  One button machine for automating feature engineering in relational databases  Hoang Thanh Lam, et al.  arXiv 
PDF
 2016  Automating Feature Engineering  Udayan Khurana, et al.  NIPS 
PDF
 2016  ExploreKit: Automatic Feature Generation and Selection  Gilad Katz, et al.  ICDM 
PDF
 2015  Deep Feature Synthesis: Towards Automating Data Science Endeavors  James Max Kanter, Kalyan Veeramachaneni  DSAA 
PDF

Hierarchical Organization of Transformations
 2016  Cognito: Automated Feature Engineering for Supervised Learning  Udayan Khurana, et al.  ICDMW 
PDF

Meta Learning
 2020  Efficient AutoML Pipeline Search with Matrix and Tensor Factorization  Chengrun Yang, et al.  arXiv 
PDF
 2017  Learning Feature Engineering for Classification  Fatemeh Nargesian, et al.  IJCAI 
PDF

Reinforcement Learning
 2017  Feature Engineering for Predictive Modeling using Reinforcement Learning  Udayan Khurana, et al.  arXiv 
PDF
 2010  Feature Selection as a OnePlayer Game  Romaric Gaudel, Michele Sebag  ICML 
PDF
Architecture Search

Evolutionary Algorithms
 2019  Evolutionary Neural AutoML for Deep Learning  Jason Liang, et al.  GECCO 
PDF
 2017  LargeScale Evolution of Image Classifiers  Esteban Real, et al.  PMLR 
PDF
 2002  Evolving Neural Networks through Augmenting Topologies  Kenneth O.Stanley, Risto Miikkulainen  Evolutionary Computation 
PDF

Local Search
 2017  Simple and Efficient Architecture Search for Convolutional Neural Networks  Thomoas Elsken, et al.  ICLR 
PDF

Meta Learning
 2016  Learning to Optimize  Ke Li, Jitendra Malik  arXiv 
PDF

Reinforcement Learning
 2018  AMC: AutoML for Model Compression and Acceleration on Mobile Devices  Yihui He, et al.  ECCV 
PDF
 2018  Efficient Neural Architecture Search via Parameter Sharing  Hieu Pham, et al.  arXiv 
PDF
 2017  Neural Architecture Search with Reinforcement Learning  Barret Zoph, Quoc V. Le  ICLR 
PDF

Transfer Learning
 2017  Learning Transferable Architectures for Scalable Image Recognition  Barret Zoph, et al.  arXiv 
PDF

Network Morphism
 2018  Efficient Neural Architecture Search with Network Morphism  Haifeng Jin, et al.  arXiv 
PDF

Continuous Optimization
 2018  Neural Architecture Optimization  Renqian Luo, et al.  arXiv 
PDF
 2019  DARTS: Differentiable Architecture Search  Hanxiao Liu, et al.  ICLR 
PDF
Frameworks
 2019  Auptimizer  an Extensible, OpenSource Framework for Hyperparameter Tuning  Jiayi Liu, et al.  IEEE Big Data 
PDF
 2019  Towards modular and programmable architecture search  Renato Negrinho, et al.  NeurIPS 
PDF
 2019  Evolutionary Neural AutoML for Deep Learning  Jason Liang, et al.  arXiv 
PDF
 2017  ATM: A Distributed, Collaborative, Scalable System for Automated Machine Learning  T. Swearingen, et al.  IEEE 
PDF
 2017  Google Vizier: A Service for BlackBox Optimization  Daniel Golovin, et al.  KDD 
PDF
 2015  AutoCompete: A Framework for Machine Learning Competitions  Abhishek Thakur, et al.  ICML 
PDF
Hyperparameter Optimization

Bayesian Optimization
 2019  Bayesian Optimization with Unknown Search Space  NeurIPS 
PDF
 2019  Constrained Bayesian optimization with noisy experiments 
PDF
 2019  Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning  NeurIPS 
PDF
 2019  Practical TwoStep Lookahead Bayesian Optimization  NeurIPS 
PDF
 2019  Predictive entropy search for multiobjective bayesian optimization with constraints 
PDF
 2018  BOCK: Bayesian optimization with cylindrical kernels  ICML 
PDF
 2018  Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features  Mojmír Mutný, et al.  NeurIPS 
PDF
 2018  HighDimensional Bayesian Optimization via Additive Models with Overlapping Groups.  PMLR 
PDF
 2018  Maximizing acquisition functions for Bayesian optimization  NeurIPS 
PDF
 2018  Scalable hyperparameter transfer learning  NeurIPS 
PDF
 2016  Bayesian Optimization with Robust Bayesian Neural Networks  Jost Tobias Springenberg， et al.  NIPS 
PDF
 2016  Scalable Hyperparameter Optimization with Products of Gaussian Process Experts  Nicolas Schilling, et al.  PKDD 
PDF
 2016  Taking the Human Out of the Loop: A Review of Bayesian Optimization  Bobak Shahriari, et al.  IEEE 
PDF
 2016  Towards AutomaticallyTuned Neural Networks  Hector Mendoza, et al.  JMLR 
PDF
 2016  TwoStage Transfer Surrogate Model for Automatic Hyperparameter Optimization  Martin Wistuba, et al.  PKDD 
PDF
 2015  Efficient and Robust Automated Machine Learning 
PDF
 2015  Hyperparameter Optimization with Factorized Multilayer Perceptrons  Nicolas Schilling, et al.  PKDD 
PDF
 2015  Hyperparameter Search Space Pruning  A New Component for Sequential ModelBased Hyperparameter Optimization  Martin Wistua, et al. 
PDF
 2015  Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons  Nicolas Schilling, et al.  ICTAI 
PDF
 2015  Learning Hyperparameter Optimization Initializations  Martin Wistuba, et al.  DSAA 
PDF
 2015  Scalable Bayesian optimization using deep neural networks  Jasper Snoek, et al.  ACM 
PDF
 2015  Sequential Modelfree Hyperparameter Tuning  Martin Wistuba, et al.  ICDM 
PDF
 2013  AutoWEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms 
PDF
 2013  Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures  J. Bergstra  JMLR 
PDF
 2012  Practical Bayesian Optimization of Machine Learning Algorithms 
PDF
 2011  Sequential ModelBased Optimization for General Algorithm Configuration(extended version) 
PDF

Evolutionary Algorithms
 2018  Autostacker: A Compositional Evolutionary Learning System  Boyuan Chen, et al.  arXiv 
PDF
 2017  LargeScale Evolution of Image Classifiers  Esteban Real, et al.  PMLR 
PDF
 2016  Automating biomedical data science through treebased pipeline optimization  Randal S. Olson, et al.  ECAL 
PDF
 2016  Evaluation of a treebased pipeline optimization tool for automating data science  Randal S. Olson, et al.  GECCO 
PDF

Lipschitz Functions
 2017  Global Optimization of Lipschitz functions  C´edric Malherbe, Nicolas Vayatis  arXiv 
PDF

Local Search
 2009  ParamILS: An Automatic Algorithm Configuration Framework  Frank Hutter, et al.  JAIR 
PDF

Meta Learning
 2019  OBOE: Collaborative Filtering for AutoML Model Selection  Chengrun Yang, et al.  KDD 
PDF
 2019  SMARTML: A Meta LearningBased Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms 
PDF
 2008  CrossDisciplinary Perspectives on MetaLearning for Algorithm Selection 
PDF

Particle Swarm Optimization
 2017  Particle Swarm Optimization for Hyperparameter Selection in Deep Neural Networks  Pablo Ribalta Lorenzo, et al.  GECCO 
PDF
 2008  Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines  ShihWei Lin, et al.  Expert Systems with Applications 
PDF

Random Search
 2016  Hyperband: A Novel BanditBased Approach to Hyperparameter Optimization  Lisha Li, et al.  arXiv 
PDF
 2012  Random Search for HyperParameter Optimization  James Bergstra, Yoshua Bengio  JMLR 
PDF
 2011  Algorithms for Hyperparameter Optimization  James Bergstra, et al.  NIPS 
PDF

Transfer Learning
 2016  Efficient Transfer Learning Method for Automatic Hyperparameter Tuning  Dani Yogatama, Gideon Mann  JMLR 
PDF
 2016  Flexible Transfer Learning Framework for Bayesian Optimisation  Tinu Theckel Joy, et al.  PAKDD 
PDF
 2016  Hyperparameter Optimization Machines  Martin Wistuba, et al.  DSAA 
PDF
 2013  Collaborative Hyperparameter Tuning  R´emi Bardenet, et al.  ICML 
PDF
Miscellaneous
 2020  Automated Machine Learning Techniques for Data Streams  AlexandruIonut Imbrea 
PDF
 2018  Accelerating Neural Architecture Search using Performance Prediction  Bowen Baker, et al.  ICLR 
PDF
 2017  Automatic Frankensteining: Creating Complex Ensembles Autonomously  Martin Wistuba, et al.  SIAM 
PDF
 2018  Characterizing classification datasets: A study of metafeatures for metalearning  Rivolli, Adriano, et al.  arXiv 
PDF
 2020  Putting the Human Back in the AutoML Loop  Xanthopoulos, Iordanis, et al.  EDBT/ICDT 
PDF
Tutorials
Bayesian Optimization
 2018  A Tutorial on Bayesian Optimization. 
PDF
 2010  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning 
PDF
Meta Learning
 2008  Metalearning  A Tutorial 
PDF
Blog
Type 
Blog Title 
Link 
HPO 
Bayesian Optimization for Hyperparameter Tuning 
Link 
MetaLearning 
Learning to learn 
Link 
MetaLearning 
Why Metalearning is Crucial for Further Advances of Artificial Intelligence? 
Link 
Books
Year of Publication 
Type 
Book Title 
Authors 
Publisher 
Link 
2009 
MetaLearning 
Metalearning  Applications to Data Mining 
Brazdil, P., Giraud Carrier, C., Soares, C., Vilalta, R. 
Springer 
Download 
2019 
HPO, MetaLearning, NAS 
AutoML: Methods, Systems, Challenges 
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren 

Download 
Projects
Project 
Type 
Language 
License 
Link 
AdaNet 
NAS 
Python 
Apache2.0 
Github 
Advisor 
HPO 
Python 
Apache2.0 
Github 
AMLA 
HPO, NAS 
Python 
Apache2.0 
Github 
ATM 
HPO 
Python 
MIT 
Github 
Auger 
HPO 
Python 
Commercial 
Homepage 
auptimizer 
HPO, NAS 
Python (support R script) 
GPL3.0 
Github 
AutoKeras 
NAS 
Python 
License 
Github 
AutoML Vision 
NAS 
Python 
Commercial 
Homepage 
AutoML Video Intelligence 
NAS 
Python 
Commercial 
Homepage 
AutoML Natural Language 
NAS 
Python 
Commercial 
Homepage 
AutoML Translation 
NAS 
Python 
Commercial 
Homepage 
AutoML Tables 
AutoFE, HPO 
Python 
Commercial 
Homepage 
autosklearn 
HPO 
Python 
License 
Github 
auto_ml 
HPO 
Python 
MIT 
Github 
BayesianOptimization 
HPO 
Python 
MIT 
Github 
BayesOpt 
HPO 
C++ 
AGPL3.0 
Github 
comet 
HPO 
Python 
Commercial 
Homepage 
DataRobot 
HPO 
Python 
Commercial 
Homepage 
DEvol 
NAS 
Python 
MIT 
Github 
DeepArchitect 
NAS 
Python 
MIT 
Github 
Determined 
HPO, NAS 
Python 
Apache2.0 
Github 
Driverless AI 
AutoFE 
Python 
Commercial 
Homepage 
FARHO 
HPO 
Python 
MIT 
Github 
H2O AutoML 
HPO 
Python, R, Java, Scala 
Apache2.0 
Github 
HpBandSter 
HPO 
Python 
BSD3Clause 
Github 
HyperBand 
HPO 
Python 
License 
Github 
Hyperopt 
HPO 
Python 
License 
Github 
Hyperoptsklearn 
HPO 
Python 
License 
Github 
Hyperparameter Hunter 
HPO 
Python 
MIT 
Github 
Katib 
HPO 
Python 
Apache2.0 
Github 
MateLabs 
HPO 
Python 
Commercial 
Github 
Milano 
HPO 
Python 
Apache2.0 
Github 
MLJAR 
HPO 
Python 
Commercial 
Homepage 
nasbot 
NAS 
Python 
MIT 
Github 
neptune 
HPO 
Python 
Commercial 
Homepage 
NNI 
HPO, NAS 
Python 
MIT 
Github 
Oboe 
HPO 
Python 
BSD3Clause 
Github 
Optunity 
HPO 
Python 
License 
Github 
R2.ai 
HPO 

Commercial 
Homepage 
RBFOpt 
HPO 
Python 
License 
Github 
RoBO 
HPO 
Python 
BSD3Clause 
Github 
ScikitOptimize 
HPO 
Python 
License 
Github 
SigOpt 
HPO 
Python 
Commercial 
Homepage 
SMAC3 
HPO 
Python 
License 
Github 
TPOT 
AutoFE, HPO 
Python 
LGPL3.0 
Github 
TransmogrifAI 
HPO 
Scala 
BSD3Clause 
Github 
Tune 
HPO 
Python 
Apache2.0 
Github 
Xcessiv 
HPO 
Python 
Apache2.0 
Github 
SmartML 
HPO 
R 
GPL3.0 
Github 
MLBox 
AutoFE, HPO 
Python 
BSD3 License 
Github 
AutoAI Watson 
AutoFE, HPO 

Commercial 
Homepage 
Slides
Type 
Slide Title 
Authors 
Link 
AutoFE 
Automated Feature Engineering for Predictive Modeling 
Udyan Khurana, etc al. 
Download 
HPO 
A Tutorial on Bayesian Optimization for Machine Learning 
Ryan P. Adams 
Download 
HPO 
Bayesian Optimisation 
Gilles Louppe 
Download 
Acknowledgement
Special thanks to everyone who contributed to this project.
Contact & Feedback
If you have any suggestions (missing papers, new papers, key researchers or typos), feel free to pull a request. Also you can mail to:
Licenses
AwesomeAutoMLPapers is available under Apache Licenses 2.0.