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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 精選自動機器學習領域的論文、文章、教學、投影片與專案。歡迎為此儲存庫按 Star，掌握這個蓬勃發展研究領域的最新動態。感謝所有為本專案貢獻的人，歡迎加入並參與貢獻。

什麼是 AutoML？

Section titled “什麼是 AutoML？”

自動機器學習（AutoML）提供方法與流程，讓非機器學習專家也能使用機器學習、提升機器學習效率並加速相關研究。

近年來，機器學習（ML）取得了顯著成果，愈來愈多學科仰賴它。然而，這些成果在很大程度上仰賴機器學習專家完成以下工作：

  • 預處理資料,
  • 選擇合適的特徵,
  • 選擇合適的模型族,
  • 最佳化模型超參數,
  • 後處理機器學習模型,
  • 審慎分析所得結果,

這些工作的複雜度往往超出非機器學習專家的能力。機器學習應用快速成長，帶動了對現成機器學習方法的需求；這些方法應易於使用，且不需專業知識。我們將以逐步自動化機器學習為目標的研究領域稱為 AutoML。作為機器學習的新興分支，AutoML 不僅受到機器學習領域關注，也受到電腦視覺、自然語言處理與圖計算領域關注。

AutoML 尚無正式定義。依據多數論文的描述，AutoML 的基本流程如下所示。

AutoML 方法已相當成熟，足以與機器學習專家競爭，有時甚至超越他們。簡言之，AutoML 能提升效能，同時節省大量時間與金錢，因為機器學習專家既難尋覓又所費不貲。因此，近年來商業界對 AutoML 的興趣大幅增加，多家大型科技公司與新創公司正開發自己的 AutoML 系統。下表概述並比較其中幾種系統。

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

Awesome-AutoML-Papers 收錄了 AutoML 所需核心技術的最新概覽：

  • 自動資料清理（Auto Clean）
  • 自動特徵工程（Auto FE）
  • 超參數最佳化（HPO）
  • 元學習
  • 神經架構搜尋（NAS）

目錄

Section titled “目錄”

論文

Section titled “論文”

綜述

Section titled “綜述”
  • 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

自動特徵工程

Section titled “自動特徵工程”
  • 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

  • 轉換的階層式組織

    Section titled “轉換的階層式組織”
    • 2016 | Cognito: Automated Feature Engineering for Supervised Learning | Udayan Khurana, et al. | ICDMW | PDF

  • 元學習

    Section titled “元學習”
    • 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

  • 強化學習

    Section titled “強化學習”
    • 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

架構搜尋

Section titled “架構搜尋”

  • 演化演算法

    Section titled “演化演算法”
    • 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

  • 區域搜尋

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

  • 元學習

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

  • 強化學習

    Section titled “強化學習”
    • 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

  • 遷移學習

    Section titled “遷移學習”
    • 2017 | Learning Transferable Architectures for Scalable Image Recognition | Barret Zoph, et al. | arXiv | PDF

  • 網路形態變換

    Section titled “網路形態變換”
    • 2019 | Auto-Keras: An Efficient Neural Architecture Search System | Haifeng Jin, et al. | KDD | PDF

  • 連續最佳化

    Section titled “連續最佳化”
    • 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

超參數最佳化

Section titled “超參數最佳化”

  • 貝葉斯最佳化

    Section titled “貝葉斯最佳化”
    • 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

  • 演化演算法

    Section titled “演化演算法”
    • 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

  • 區域搜尋

    Section titled “區域搜尋”
    • 2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR | PDF

  • 元學習

    Section titled “元學習”
    • 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

  • 粒子群最佳化

    Section titled “粒子群最佳化”
    • 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

  • 隨機搜尋

    Section titled “隨機搜尋”
    • 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

  • 遷移學習

    Section titled “遷移學習”
    • 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

教學

Section titled “教學”

貝葉斯最佳化

Section titled “貝葉斯最佳化”
  • 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

元學習

Section titled “元學習”
  • 2008 | Metalearning - A Tutorial | PDF

部落格

Section titled “部落格”
類型部落格標題連結
HPOBayesian Optimization for Hyperparameter TuningLink
Meta-LearningLearning to learnLink
Meta-LearningWhy Meta-learning is Crucial for Further Advances of Artificial Intelligence?Link

書籍

Section titled “書籍”
出版年份類型書名作者出版社連結
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

影片

Section titled “影片”

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

專案

Section titled “專案”
專案類型語言授權連結
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

投影片

Section titled “投影片”
類型投影片標題作者連結
AutoFEAutomated Feature Engineering for Predictive ModelingUdyan Khurana, etc al.Download
HPOA Tutorial on Bayesian Optimization for Machine LearningRyan P. AdamsDownload
HPOBayesian OptimisationGilles LouppeDownload

致謝

Section titled “致謝”

特別感謝所有為本專案作出貢獻的人。

姓名簡介
Alexander Robles巴西 UNICAMP 博士生
derekflint
endymecy騰訊資深研究員
Eric
Erin LeDellH2O.ai 首席機器學習科學家
fwcore
Gaurav Mittal
Hernan Ceferino Vazquez博士，MercadoLibre 資料科學專家
Kaustubh Damania
Lilian BessonCentraleSupélec 博士生
罗磊
Marc
Mohamed Maher
Neil ConwayDetermined AI 技術長
Richard Liaw加州大學柏克萊分校博士生
Randy OlsonLifeEGX 首席資料科學家
Slava KurilyakProduvia 創辦人兼執行長
Saket Maheshwary人工智慧研究員
shaido987
sophia-wright-blue
tengben0905
xuehui@Microsoft
Yihui He卡內基美隆大學研究生

聯絡與意見回饋

Section titled “聯絡與意見回饋”

若有任何建議（遺漏的論文、新論文、重要研究人員或錯字），歡迎提出請求，也可以寄信至：

授權

Section titled “授權”

Awesome-AutoML-Papers 採用 Apache License 2.0。

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