Awesome-AutoML-Papers includes very up-to-date overviews of the bread-and-butter techniques we need in AutoML:
- Automated Data Clean (Auto Clean)
- Automated Feature Enginnering (Auto FE)
- Hyperparameter Optimization (HPO)
- Meta-Learning
- Neural Architecture Search (NAS)
Table of Contents
Papers
Surveys
- 2019 | AutoML: A Survey of the State-of-the-Art | Xin He, et al. | arXiv |
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- 2019 | Survey on Automated Machine Learning | Marc Zoeller, Marco F. Huber | arXiv |
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- 2019 | Automated Machine Learning: State-of-The-Art and Open Challenges | Radwa Elshawi, et al. | arXiv |
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- 2018 | Taking Human out of Learning Applications: A Survey on Automated Machine Learning | Quanming Yao, et al. | arXiv |
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Automated Feature Engineering
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Expand Reduce
- 2017 | AutoLearn — Automated Feature Generation and Selection | Ambika Kaul, et al. | ICDM |
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- 2017 | One button machine for automating feature engineering in relational databases | Hoang Thanh Lam, et al. | arXiv |
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- 2016 | Automating Feature Engineering | Udayan Khurana, et al. | NIPS |
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- 2016 | ExploreKit: Automatic Feature Generation and Selection | Gilad Katz, et al. | ICDM |
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- 2015 | Deep Feature Synthesis: Towards Automating Data Science Endeavors | James Max Kanter, Kalyan Veeramachaneni | DSAA |
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Hierarchical Organization of Transformations
- 2016 | Cognito: Automated Feature Engineering for Supervised Learning | Udayan Khurana, et al. | ICDMW |
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Meta Learning
- 2017 | Learning Feature Engineering for Classification | Fatemeh Nargesian, et al. | IJCAI |
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Reinforcement Learning
- 2017 | Feature Engineering for Predictive Modeling using Reinforcement Learning | Udayan Khurana, et al. | arXiv |
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- 2010 | Feature Selection as a One-Player Game | Romaric Gaudel, Michele Sebag | ICML |
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Architecture Search
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Evolutionary Algorithms
- 2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | arXiv |
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- 2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR |
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- 2002 | Evolving Neural Networks through Augmenting Topologies | Kenneth O.Stanley, Risto Miikkulainen | Evolutionary Computation |
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Local Search
- 2017 | Simple and Efficient Architecture Search for Convolutional Neural Networks | Thomoas Elsken, et al. | ICLR |
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Meta Learning
- 2016 | Learning to Optimize | Ke Li, Jitendra Malik | arXiv |
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Reinforcement Learning
- 2018 | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | Yihui He, et al. | ECCV |
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- 2018 | Efficient Neural Architecture Search via Parameter Sharing | Hieu Pham, et al. | arXiv |
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- 2017 | Neural Architecture Search with Reinforcement Learning | Barret Zoph, Quoc V. Le | ICLR |
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Transfer Learning
- 2017 | Learning Transferable Architectures for Scalable Image Recognition | Barret Zoph, et al. | arXiv |
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Network Morphism
- 2018 | Efficient Neural Architecture Search with Network Morphism | Haifeng Jin, et al. | arXiv |
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Continuous Optimization
- 2018 | Neural Architecture Optimization | Renqian Luo, et al. | arXiv |
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- 2019 | DARTS: Differentiable Architecture Search | Hanxiao Liu, et al. | ICLR |
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Frameworks
- 2019 | Towards modular and programmable architecture search | Renato Negrinho, et al. | NeurIPS |
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- 2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | arXiv |
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- 2017 | ATM: A Distributed, Collaborative, Scalable System for Automated Machine Learning | T. Swearingen, et al. | IEEE |
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- 2017 | Google Vizier: A Service for Black-Box Optimization | Daniel Golovin, et al. | KDD |
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- 2015 | AutoCompete: A Framework for Machine Learning Competitions | Abhishek Thakur, et al. | ICML |
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Hyperparameter Optimization
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Bayesian Optimization
- 2018 | A Tutorial on Bayesian Optimization. |
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- 2018 | Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | Mojmír Mutný, et al. | NeurIPS |
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- 2018 | High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. | PMLR |
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- 2016 | Bayesian Optimization with Robust Bayesian Neural Networks | Jost Tobias Springenberg, et al. | NIPS |
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- 2016 | Scalable Hyperparameter Optimization with Products of Gaussian Process Experts | Nicolas Schilling, et al. | PKDD |
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- 2016 | Taking the Human Out of the Loop: A Review of Bayesian Optimization | Bobak Shahriari, et al. | IEEE |
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- 2016 | Towards Automatically-Tuned Neural Networks | Hector Mendoza, et al. | JMLR |
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- 2016 | Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization | Martin Wistuba, et al. | PKDD |
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- 2015 | Efficient and Robust Automated Machine Learning |
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- 2015 | Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | PKDD |
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- 2015 | Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization | Martin Wistua, et al. |
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- 2015 | Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | ICTAI |
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- 2015 | Learning Hyperparameter Optimization Initializations | Martin Wistuba, et al. | DSAA |
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- 2015 | Scalable Bayesian optimization using deep neural networks | Jasper Snoek, et al. | ACM |
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- 2015 | Sequential Model-free Hyperparameter Tuning | Martin Wistuba, et al. | ICDM |
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- 2013 | Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms |
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- 2013 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures | J. Bergstra | JMLR |
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- 2012 | Practical Bayesian Optimization of Machine Learning Algorithms |
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- 2011 | Sequential Model-Based Optimization for General Algorithm Configuration(extended version) |
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Evolutionary Algorithms
- 2018 | Autostacker: A Compositional Evolutionary Learning System | Boyuan Chen, et al. | arXiv |
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- 2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR |
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- 2016 | Automating biomedical data science through tree-based pipeline optimization | Randal S. Olson, et al. | ECAL |
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- 2016 | Evaluation of a tree-based pipeline optimization tool for automating data science | Randal S. Olson, et al. | GECCO |
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Lipschitz Functions
- 2017 | Global Optimization of Lipschitz functions | C´edric Malherbe, Nicolas Vayatis | arXiv |
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Local Search
- 2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR |
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Meta Learning
- 2008 | Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection |
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- 2019 | SMARTML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms |
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Particle Swarm Optimization
- 2017 | Particle Swarm Optimization for Hyper-parameter Selection in Deep Neural Networks | Pablo Ribalta Lorenzo, et al. | GECCO |
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- 2008 | Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines | Shih-Wei Lin, et al. | Expert Systems with Applications |
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Random Search
- 2016 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | Lisha Li, et al. | arXiv |
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- 2012 | Random Search for Hyper-Parameter Optimization | James Bergstra, Yoshua Bengio | JMLR |
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- 2011 | Algorithms for Hyper-parameter Optimization | James Bergstra, et al. | NIPS |
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Transfer Learning
- 2016 | Efficient Transfer Learning Method for Automatic Hyperparameter Tuning | Dani Yogatama, Gideon Mann | JMLR |
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- 2016 | Flexible Transfer Learning Framework for Bayesian Optimisation | Tinu Theckel Joy, et al. | PAKDD |
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- 2016 | Hyperparameter Optimization Machines | Martin Wistuba, et al. | DSAA |
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- 2013 | Collaborative Hyperparameter Tuning | R´emi Bardenet, et al. | ICML |
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Miscellaneous
- 2018 | Accelerating Neural Architecture Search using Performance Prediction | Bowen Baker, et al. | ICLR |
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- 2017 | Automatic Frankensteining: Creating Complex Ensembles Autonomously | Martin Wistuba, et al. | SIAM |
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Tutorials
Bayesian Optimization
- 2010 | A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning |
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Meta Learning
- 2008 | Metalearning - A Tutorial |
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Blog
Type |
Blog Title |
Link |
HPO |
Bayesian Optimization for Hyperparameter Tuning |
Link |
Meta-Learning |
Learning to learn |
Link |
Meta-Learning |
Why Meta-learning is Crucial for Further Advances of Artificial Intelligence? |
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Books
Year of Publication |
Type |
Book Title |
Authors |
Publisher |
Link |
2009 |
Meta-Learning |
Metalearning - Applications to Data Mining |
Brazdil, P., Giraud Carrier, C., Soares, C., Vilalta, R. |
Springer |
Download |
2019 |
HPO, Meta-Learning, NAS |
AutoML: Methods, Systems, Challenges |
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren |
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Download |
Projects
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 |