[TOC]
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Surveys
- 2023 A General Recipe for Automated Machine Learning in Practice
- 2023 AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
- 2023 Eight years of AutoML categorisation, review and trends
- 2023 AutoML in The Wild: Obstacles, Workarounds, and Expectations
- 2023 AutoML from Software Engineering Perspective: Landscapes and Challenges
- 2022 AutoML: state of the art with a focus on anomaly detection, challenges, and research directions
- 2022 AutoML for Deep Recommender Systems: A Survey
- 2022 A Comprehensive Survey on Automated Machine Learning for Recommendations
- 2022 Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
- 2021 Survey on Automated Machine Learning (AutoML) and Meta learning
- 2021 AutoML for Multi-Label Classification: Overview and Empirical Evaluation
- 2021 Automated Machine Learning on Graphs: A Survey
- 2020 AutoML to Date and Beyond: Challenges and Opportunities
- 2019 Automated Machine Learning: State-of-The-Art and Open Challenges
- 2019 Benchmark and Survey of Automated Machine Learning Frameworks
- 2019 AutoML: A Survey of the State-of-the-Art
- 2018 Taking Human out of Learning Applications: A Survey on Automated Machine Learning
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AutoClean (Data Augmentation)
- 2023 AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
- 2023 DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
- 2023 Kitana: Efficient Data Augmentation Search for AutoML
- 2023 Rethinking Data Augmentation for Tabular Data in Deep Learning
- 2023 Retrieval-Based Transformer for Table Augmentation
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AutoFE
- 2024 Automated data processing and feature engineering for deep learning and big data applications: a survey
- 2022 BigFeat: Scalable and Interpretable Automated Feature Engineering Framework
- 2022 DIFER: Differentiable Automated Feature Engineering
- 2022 OpenFE: Automated Feature Generation with Expert-level Performance
- 2020 Safe: Scalable automatic feature engineering framework for industrial tasks
- 2020 DAFEE: a scalable distributed automatic feature engineering algorithm for relational datasets
- 2019 The autofeat Python Library for Automated Feature Engineering and Selection
- 2017 Learning Feature Engineering for Classification
- 2017 AutoLearn — Automated Feature Generation and Selection
- 2016 Cognito: Automated Feature Engineering for Supervised Learning
- 2016 ExploreKit: Automatic Feature Generation and Selection
- 2015 Deep Feature Synthesis: Towards Automating Data Science Endeavors
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HPO
- 2023 On the Hyperparameter Landscapes of Machine Learning Algorithms
- 2023 Transfer Learning for Bayesian Optimization: A Survey
- 2021 Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
- 2020 On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
- 2018 A tutorial on bayesian optimization
- 2016 Hyperband: A novel bandit-based approach to hyperparameter optimization
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NAS
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Meta Learning
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AutoML with LLM
- 2024 Large Language Model Agent for Hyper-Parameter Optimization
- 2023 Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
- 2023 AutoML-GPT: Large Language Model for AutoML
- 2023 TabLLM: Few-shot Classification of Tabular Data with Large Language Models
- 2023 ChatGPT as your Personal Data Scientist
- 2023 MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
- 2023 Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
- 2023 AutoML-GPT: Automatic Machine Learning with GPT
- 2022 Can Foundation Models Wrangle Your Data?
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AutoML for specified task
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Benchmarks
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Open Source
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2021 LightAutoML [Paper]
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2021 AutoX
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2020 H2O AutoML [Paper]
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2019 NNI
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2019 Mljar
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2018 Transmogrif AI
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2017 MLBox
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2015 Auto-Sklearn [Paper]
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2014 Hyperopt-Sklearn [Paper]
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2020 AutoDL automl for deep learning
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2019 Auto-PyTorch Paper backend with pytorch
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2018 AdaNet backend with tensorflow
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2017 AutoKeras backend with Keras
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2020 AutoGL automl for graph learning
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2019 AutoTS automl for time series
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Commercial Platforms
- AutoML Conference2023
- AutoML Conference2022
- AutoML 2021(AutoML Workshop at ICML 2021)
- AutoML 2020(AutoML Workshop at ICML 2020)
- AutoML 2019(AutoML Workshop at ICML 2019)
- AutoML 2018(AutoML Workshop at ICML 2018)
- AutoML 2017(AutoML Workshop at ICML 2017)
- AutoML 2016(AutoML Workshop at ICML 2016)
- AutoML 2015(AutoML Workshop at ICML 2015)
- AutoML 2014(AutoML Workshop at ICML 2014)
- AutoML 2022(6th KDD workshop on AutoML)
- AutoML 2021(5th KDD workshop on AutoML)
- AutoML 2020(4th KDD workshop on AutoML)
- AutoML 2019(3rd KDD workshop on AutoML)
- AutoML 2018(2nd KDD workshop on AutoML and Big Data)
- AutoML 2017(KDD workshop on AutoML)
- 2023 A Decade of AutoML: Reflections and the Road Ahead
- 2023 AutoML: Replacing Data Scientists?
- 2022 The AutoML Landscape
- 2021 AutoML: A Perspective where Industry Meets Academy
- 2020 Automated Machine Learning in Action
- 2019 Automated Machine Learning Methods, Systems, Challenges
- 2019 AutoDL Challenge
- 2019 AutoWSL Challenge
- 2019 AutoSeries Challenge
- 2019 AutoSpeech Challenge
- 2019 AutoNLP Challenge
- 2019 AutoCV2 Challenge
- 2018 Third AutoML Challenge
- 2017~2018 Second AutoML Challenge
- 2015~2016 First AutoML Challenge