A curated list of awesome automated machine learning resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers awesome-architecture-search, and awesome-NAS.

Please feel free to pull requests or open an issue to add papers. Markdown format:

- Paper Name [[pdf]](link) [[code]](link)
  - Author 1, Author 2, Author 3. Conference Year

• Automated Feature Engieerning
  • Exploration and Reduction
  • Reinforcement Learning
• Automated Model Selection and Learning
  • Bayesian Optimization
  • Evolutionary Algorithm
  • Gradient-based Optimization
• Automated Deep Learning
  • Bayesian Optimization
  • Reinforcement Learning
  • Evolutionary Algorithm
  • Gradient-based Optimization
  • Test Performance on CIFAR-10
• Survey
• Related Resources

• Deep Feature Synthesis: Towards Automating Data Science Endeavors [pdf] [code]
  • James Max Kanter, Kalyan Veeramachaneni.
• ExploreKit: Automatic Feature Generation and Selection [pdf] [code]
  • Gilad Katz, Eui Chul Richard Shin, Dawn Song. ICDM 2016

• Feature Engineering for Predictive Modeling using Reinforcement Learning [pdf]
  • Udayan Khurana, Horst Samulowitz, Deepak Turaga, AAAI 2018

• Efficient and Robust Automated Machine Learning [pdf] [code]
  • Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter. NIPS 2015
• Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms [pdf] [code]
  • Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown. KDD 2013

• Automating Biomedical Data Science Through Tree-Based Pipeline Optimization [pdf] [code]
  • Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore. EvoApplications 2016

• TODO

• Auto-Keras: Efficient Neural Architecture Search with Network Morphism [pdf] [code]
  • Haifeng Jin, Qingquan Song, Xia Hu. arXiv 1806

• Neural Architecture Search with Reinforcement Learning (NAS) [pdf] [unofficial code]
  • Barret Zoph and Quoc V. Le. ICLR 2017
• Learning Transferable Architectures for Scalable Image Recognition (NASNet) [pdf] [nasnet]
  • Barret Zoph, Vijay Vasudevan, Jonathan Shlens, Quoc V. Le. CVPR 2018
• Efficient Neural Architecture Search via Parameter Sharing (ENAS) [pdf] [code]
  • Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. ICML 2018
• Path-Level Network Transformation for Efficient Architecture Search (PathLevel-EAS) [pdf] [code]
  • Han Cai, Jiacheng Yang, Weinan Zhang, Song Han,Yong Yu. ICML 2018

• Large-Scale Evolution of Image Classifiers (LargeEvoNet) [pdf] [code]
  • Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, Alex Kurakin. ICML 2017
• Regularized Evolution for Image Classifier Architecture Search (AmoebaNet) [pdf] [code] [code]
  • Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le. AAAI 2019

• DARTS: Differentiable Architecture Search [pdf] [code]
  • Hanxiao Liu, Karen Simonyan, Yiming Yang. ICLR 2019
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [pdf] [code]
  • Han Cai, Ligeng Zhu, Song Han. ICLR 2019

• Techniques for Automated Machine Learning [pdf]
  • Yi-Wei Chen, Qingquan Song, Xia Hu. arXiv 1907
• Neural Architecture Search: A Survey [pdf]
  • Thomas Elsken, Jan Hendrik Metzen, Frank Hutter. arXiv 1808
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning [pdf]
  • Yao Quanming, Wang Mengshuo, Jair Escalante Hugo, Guyon Isabelle, Hu Yi-Qi, Li Yu-Feng, Tu Wei-Wei, Yang Qiang, Yu Yang. arXiv 1810

• hibayesian/awesome-automl-papers
• markdtw/awesome-architecture-search
• AutoML.org/Literature on Neural Architecture Search
• D-X-Y/awesome-NAS
• weiaicunzai/awesome-image-classification
• ChanChiChoi/awesome-automl
• songzhaozhe/Model-Architecture-Search-Paper-List