/awesome-automated-machine-learning

A curated list of awesome automated machine learning resources

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Awesome Automated Machine Learning Awesome

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

Table of Contents

Automated Feature Engineering

Exploration and Reduction

  • 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

Reinforcement Learning

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

Automated Model Selection and Learning

Bayesian Optimization

  • 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

Evolutionary Algorithm

  • 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

Gradient-based Optimization

  • TODO

Automated Deep Learning

Bayesian Optimization

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

Reinforcement Learning

  • 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

Evolutionary Algorithm

  • 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

Gradient-based Optimization

  • 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

Survey

  • 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

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