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AutoML Papers

Source: https://github.com/hibayesian/awesome-automl-papers at 0317821496b4
License: Apache-2.0
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Awesome-AutoML-Papers

Section titled “Awesome-AutoML-Papers”

Awesome-AutoML-Papers 汇集了自动机器学习领域的论文、文章、教程、幻灯片和项目。欢迎为本仓库点 Star，以了解这一蓬勃发展的研究领域的最新进展。感谢所有为本项目作出贡献的人。欢迎加入我们并参与贡献。

什么是 AutoML？

Section titled “什么是 AutoML？”

自动机器学习（AutoML）提供相关方法与流程，让非机器学习专家也能使用机器学习，提高机器学习效率并加速机器学习研究。

近年来，机器学习（ML）取得了显著成果，越来越多的学科依赖机器学习。然而，这些成功在很大程度上依赖机器学习专家完成以下任务：

  • 预处理数据,
  • 选择合适的特征,
  • 选择合适的模型族,
  • 优化模型超参数,
  • 后处理机器学习模型,
  • 严格分析所得结果,

这些任务往往超出非机器学习专家的能力范围。机器学习应用的迅速增长催生了对现成机器学习方法的需求，这些方法应易于使用且无需专业知识。我们将旨在逐步实现机器学习自动化的研究领域称为 AutoML。作为机器学习的一个新兴分支，AutoML 不仅在机器学习领域受到关注，也在计算机视觉、自然语言处理和图计算领域受到关注。

AutoML 尚无正式定义。根据大多数论文中的描述，AutoML 的基本流程如下所示。

AutoML 方法已足够成熟，可以与机器学习专家竞争，有时甚至胜过他们。简而言之，AutoML 能够提升性能，同时节省大量时间和资金，因为机器学习专家既难以寻觅又成本高昂。因此，近年来商业领域对 AutoML 的兴趣大幅增长，多家大型科技公司和初创公司正在开发自己的 AutoML 系统。下表概述并比较了其中一些系统。

公司AutoFEHPONAS
4paradigm√√×
Alibaba×√×
Baidu××√
Determined AI×√√
Google√√√
DataCanvas√√√
H2O.ai√√×
Microsoft×√√
MLJAR√√√
RapidMiner√√×
Tencent×√×

Awesome-AutoML-Papers 收录了 AutoML 所需核心技术的最新综述：

  • 自动数据清理（Auto Clean）
  • 自动特征工程（Auto FE）
  • 超参数优化（HPO）
  • 元学习
  • 神经架构搜索（NAS）

目录

Section titled “目录”

论文

Section titled “论文”

综述

Section titled “综述”
  • 2019 | AutoML: A Survey of the State-of-the-Art | Xin He, et al. | arXiv | PDF
  • 2019 | Survey on Automated Machine Learning | Marc Zoeller, Marco F. Huber | arXiv | PDF
  • 2019 | Automated Machine Learning: State-of-The-Art 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 Learning—a brief review at the end of the early years | Escalante, H. J. | arXiv | PDF
  • 2022 | IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective | Li Yang, et al. | arXiv | PDF
  • 2024 | Automated machine learning: past, present and future | Baratchi. M, et al. | Artificial Intelligence Review | Springer

自动特征工程

Section titled “自动特征工程”
  • 2022 | BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML | Mehdi Bahrami, et al. | AutoML | PDF
  • 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

  • 转换的层次化组织

    Section titled “转换的层次化组织”
    • 2016 | Cognito: Automated Feature Engineering for Supervised Learning | Udayan Khurana, et al. | ICDMW | PDF

  • 元学习

    Section titled “元学习”
    • 2020 | AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space | Chengrun Yang, et al. | KDD | PDF
    • 2017 | Learning Feature Engineering for Classification | Fatemeh Nargesian, et al. | IJCAI | PDF

  • 强化学习

    Section titled “强化学习”
    • 2017 | Feature Engineering for Predictive Modeling using Reinforcement Learning | Udayan Khurana, et al. | arXiv | PDF
    • 2010 | Feature Selection as a One-Player Game | Romaric Gaudel, Michele Sebag | ICML | PDF

架构搜索

Section titled “架构搜索”

  • 进化算法

    Section titled “进化算法”
    • 2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | GECCO | PDF
    • 2017 | Large-Scale 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

  • 局部搜索

    Section titled “局部搜索”
    • 2017 | Simple and Efficient Architecture Search for Convolutional Neural Networks | Thomoas Elsken, et al. | ICLR | PDF

  • 元学习

    Section titled “元学习”
    • 2016 | Learning to Optimize | Ke Li, Jitendra Malik | arXiv | PDF

  • 强化学习

    Section titled “强化学习”
    • 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

  • 迁移学习

    Section titled “迁移学习”
    • 2017 | Learning Transferable Architectures for Scalable Image Recognition | Barret Zoph, et al. | arXiv | PDF

  • 网络形态变换

    Section titled “网络形态变换”
    • 2019 | Auto-Keras: An Efficient Neural Architecture Search System | Haifeng Jin, et al. | KDD | PDF

  • 连续优化

    Section titled “连续优化”
    • 2018 | Neural Architecture Optimization | Renqian Luo, et al. | arXiv | PDF
    • 2019 | DARTS: Differentiable Architecture Search | Hanxiao Liu, et al. | ICLR | PDF
    • 2021 | SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | Pyeon, et al. | ICLR | PDF

框架

Section titled “框架”
  • 2019 | Auptimizer — an Extensible, Open-Source 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 Black-Box Optimization | Daniel Golovin, et al. | KDD |PDF
  • 2015 | AutoCompete: A Framework for Machine Learning Competitions | Abhishek Thakur, et al. | ICML | PDF

超参数优化

Section titled “超参数优化”

  • 贝叶斯优化

    Section titled “贝叶斯优化”
    • 2020 | Bayesian Optimization of Risk Measures | NeurIPS | PDF
    • 2020 | BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization | NeurIPS | PDF
    • 2020 | Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly | JMLR | PDF
    • 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 Two-Step Lookahead Bayesian Optimization | NeurIPS | PDF
    • 2019 | Predictive entropy search for multi-objective 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 | High-Dimensional 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 Automatically-Tuned Neural Networks | Hector Mendoza, et al. | JMLR | PDF
    • 2016 | Two-Stage 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 Model-Based 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 Model-free Hyperparameter Tuning | Martin Wistuba, et al. | ICDM | PDF
    • 2013 | Auto-WEKA: 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 Model-Based Optimization for General Algorithm Configuration(extended version) | PDF

  • 进化算法

    Section titled “进化算法”
    • 2020 | Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians | Juhan Bae, Roger Grosse | Neurips | PDF
    • 2018 | Autostacker: A Compositional Evolutionary Learning System | Boyuan Chen, et al. | arXiv | PDF
    • 2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR | PDF
    • 2016 | Automating biomedical data science through tree-based pipeline optimization | Randal S. Olson, et al. | ECAL | PDF
    • 2016 | Evaluation of a tree-based pipeline optimization tool for automating data science | Randal S. Olson, et al. | GECCO | PDF

  • Lipschitz Functions

    Section titled “Lipschitz Functions”
    • 2017 | Global Optimization of Lipschitz functions | C´edric Malherbe, Nicolas Vayatis | arXiv | PDF

  • 局部搜索

    Section titled “局部搜索”
    • 2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR | PDF

  • 元学习

    Section titled “元学习”
    • 2019 | OBOE: Collaborative Filtering for AutoML Model Selection | Chengrun Yang, et al. | KDD | PDF
    • 2019 | SMARTML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms | PDF
    • 2008 | Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection | PDF

  • 粒子群优化

    Section titled “粒子群优化”
    • 2017 | Particle Swarm Optimization for Hyper-parameter 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 | Shih-Wei Lin, et al. | Expert Systems with Applications | PDF

  • 随机搜索

    Section titled “随机搜索”
    • 2016 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | Lisha Li, et al. | arXiv | PDF
    • 2012 | Random Search for Hyper-Parameter Optimization | James Bergstra, Yoshua Bengio | JMLR | PDF
    • 2011 | Algorithms for Hyper-parameter Optimization | James Bergstra, et al. | NIPS | PDF

  • 迁移学习

    Section titled “迁移学习”
    • 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

其他

Section titled “其他”
  • 2020 | Automated Machine Learning Techniques for Data Streams | Alexandru-Ionut 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 meta-features for meta-learning | Rivolli, Adriano, et al. | arXiv | PDF
  • 2020 | Putting the Human Back in the AutoML Loop | Xanthopoulos, Iordanis, et al. | EDBT/ICDT | PDF

教程

Section titled “教程”

贝叶斯优化

Section titled “贝叶斯优化”
  • 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

元学习

Section titled “元学习”
  • 2008 | Metalearning - A Tutorial | PDF

博客

Section titled “博客”
类型博客标题链接
HPOBayesian Optimization for Hyperparameter TuningLink
Meta-LearningLearning to learnLink
Meta-LearningWhy Meta-learning is Crucial for Further Advances of Artificial Intelligence?Link

图书

Section titled “图书”
年份类型书名作者出版社链接
2009Meta-LearningMetalearning - Applications to Data MiningBrazdil, P., Giraud Carrier, C., Soares, C., Vilalta, R.SpringerDownload
2019HPO, Meta-Learning, NASAutoML: Methods, Systems, ChallengesFrank Hutter, Lars Kotthoff, Joaquin VanschorenDownload
2021LearningAutomated Machine Learning in ActionQinquan Song, Haifeng Jin, Xia HuManning PublicationsDownload

视频

Section titled “视频”

| 标题 | 作者 | 链接 | | AutoML Basics: Automated Machine Learning in Action | Qinquan Song, Haifeng Jin, Xia Hu | (https://www.youtube.com/watch?v=9KpieG0B7VM) |

项目

Section titled “项目”
项目类型语言许可证链接
AdaNetNASPythonApache-2.0Github
AdvisorHPOPythonApache-2.0Github
AMLAHPO, NASPythonApache-2.0Github
ATMHPOPythonMITGithub
AugerHPOPythonCommercialHomepage
auptimizerHPO, NASPython (support R script)GPL-3.0Github
Auto-KerasNASPythonLicenseGithub
AutoML VisionNASPythonCommercialHomepage
AutoML Video IntelligenceNASPythonCommercialHomepage
AutoML Natural LanguageNASPythonCommercialHomepage
AutoML TranslationNASPythonCommercialHomepage
AutoML TablesAutoFE, HPOPythonCommercialHomepage
AutoPyTorchHPO, NASPythonApache-2.0Github
HyperGBMHPOPythonPythonGithub
HyperKerasNASPythonPythonGithub
HypernetsHPO, NASPythonPythonGithub
auto-sklearnHPOPythonLicenseGithub
auto_mlHPOPythonMITGithub
BayesianOptimizationHPOPythonMITGithub
BayesOptHPOC++AGPL-3.0Github
cometHPOPythonCommercialHomepage
DataRobotHPOPythonCommercialHomepage
DEvolNASPythonMITGithub
DeepArchitectNASPythonMITGithub
DeterminedHPO, NASPythonApache-2.0Github
Driverless AIAutoFEPythonCommercialHomepage
FAR-HOHPOPythonMITGithub
H2O AutoMLHPOPython, R, Java, ScalaApache-2.0Github
HpBandSterHPOPythonBSD-3-ClauseGithub
HyperBandHPOPythonLicenseGithub
HyperoptHPOPythonLicenseGithub
Hyperopt-sklearnHPOPythonLicenseGithub
Hyperparameter HunterHPOPythonMITGithub
KatibHPOPythonApache-2.0Github
MateLabsHPOPythonCommercialGithub
MilanoHPOPythonApache-2.0Github
MLJARAutoFE, HPO, NASPythonMITGithub
mlr3automlHPORLGPL-3.0GitHub
nasbotNASPythonMITGithub
neptuneHPOPythonCommercialHomepage
NNIHPO, NASPythonMITGithub
OboeHPOPythonBSD-3-ClauseGithub
OptunityHPOPythonLicenseGithub
R2.aiHPOCommercialHomepage
RBFOptHPOPythonLicenseGithub
RoBOHPOPythonBSD-3-ClauseGithub
Scikit-OptimizeHPOPythonLicenseGithub
SigOptHPOPythonCommercialHomepage
SMAC3HPOPythonLicenseGithub
TPOTAutoFE, HPOPythonLGPL-3.0Github
TransmogrifAIHPOScalaBSD-3-ClauseGithub
TuneHPOPythonApache-2.0Github
XcessivHPOPythonApache-2.0Github
SmartMLHPORGPL-3.0Github
MLBoxAutoFE, HPOPythonBSD-3 LicenseGithub
AutoAI WatsonAutoFE, HPOCommercialHomepage
AUtoMLAutoMLPythonMITGithub
OptunaHPOPythonMITGithub

幻灯片

Section titled “幻灯片”
类型幻灯片标题作者链接
AutoFEAutomated Feature Engineering for Predictive ModelingUdyan Khurana, etc al.Download
HPOA Tutorial on Bayesian Optimization for Machine LearningRyan P. AdamsDownload
HPOBayesian OptimisationGilles LouppeDownload

致谢

Section titled “致谢”

特别感谢所有为本项目作出贡献的人。

姓名简介
Alexander Robles巴西 UNICAMP 博士生
derekflint
endymecy腾讯高级研究员
Eric
Erin LeDellH2O.ai 首席机器学习科学家
fwcore
Gaurav Mittal
Hernan Ceferino Vazquez博士，MercadoLibre 数据科学专家
Kaustubh Damania
Lilian BessonCentraleSupélec 博士生
罗磊
Marc
Mohamed Maher
Neil ConwayDetermined AI 首席技术官
Richard Liaw加州大学伯克利分校博士生
Randy OlsonLifeEGX 首席数据科学家
Slava KurilyakProduvia 创始人兼首席执行官
Saket Maheshwary人工智能研究员
shaido987
sophia-wright-blue
tengben0905
xuehui@Microsoft
Yihui He卡内基梅隆大学研究生

联系与反馈

Section titled “联系与反馈”

如果你有任何建议（缺失的论文、新论文、重要研究人员或拼写错误），欢迎提交请求。也可以发送邮件至：

许可

Section titled “许可”

Awesome-AutoML-Papers 采用 Apache License 2.0。

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