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Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines.
Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2007
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Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
Chris Thornton, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2013
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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Agile planning for real-world disaster response
Feng Wu, Sarvapali D. Ramchurn, Wenchao Jiang, Jeol E. Fischer, Tom Rodden, and Nicholas R. Jennings · 2015
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Tpot: A tree-based pipeline optimization tool for automating machine learning
Randal S Olson and Jason H Moore · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, L. Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, L. Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Monte-carlo planning for agile legged locomotion
Patrick Clary, Pedro Morais, Alan Fern, and Jonathan Hurst · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Marwin Segler, Mike Preuss, and Mark Waller · 2018
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Dec-mcts: Decentralized planning for multi-robot active perception
Graeme Best, Oliver M Cliff, Timothy Patten, Ramgopal R Mettu, and Robert Fitch · 2019
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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Autogluon-tabular: Robust and accurate automl for structured data, 2020
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
Cited alongside, same era.
Auto-sklearn 2.0: Hands-free automl via meta-learning, 2020
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2020
Cited alongside, same era.
H2O AutoML: Scalable automatic machine learning
Erin LeDell and Sebastien Poirier · 2020
Cited alongside, same era.
Flaml: A fast and lightweight automl library
Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu · 2021
Cited alongside, same era.
Alphazero-like tree-search can guide large language model decoding and training, 2023
Xidong Feng, Ziyu Wan, Muning Wen, Ying Wen, Weinan Zhang, and Jun Wang · 2023
Cited alongside, same era.
Autokeras: An automl library for deep learning
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu · 2023
Large language models for automated data science: Introducing caafe for context-aware automated feature engineering, 2024
Noah Hollmann, Samuel Müller, and Frank Hutter · 2024
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Rot: Enhancing large language models with reflection on search trees, 2024
Wenyang Hui and Kewei Tu · 2024
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Can large language models explore in-context?, 2024
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, and Aleksandrs Slivkins · 2024
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Exploring large language models for feature selection: A data-centric perspective, 2024
Dawei Li, Zhen Tan, and Huan Liu · 2024
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Large language model agent for hyper-parameter optimization
Siyi Liu, Chen Gao, and Yong Li · 2024
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Cited alongside, same era.
Introducing Claude 3.5 Sonnet — anthropic.com
Anthropic · 2024
Cited alongside, same era.
Thoughtsculpt: Reasoning with intermediate revision and search, 2024
Yizhou Chi, Kevin Yang, and Dan Klein · 2024
Cited alongside, same era.
Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024
DeepSeek-AI · 2024
Cited alongside, same era.
Efficient exploration for llms, 2024
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy · 2024
Cited alongside, same era.
Amlb: an automl benchmark
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, and Joaquin Vanschoren · 2024
Cited alongside, same era.
Ds-agent: Automated data science by empowering large language models with case-based reasoning, 2024
Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, and Jun Wang · 2024
Cited alongside, same era.
Daqin Luo, Chengjian Feng, Yuxuan Nong, and Yiqing Shen · 2024
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Hello GPT-4o
OpenAI · 2024
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Aide: Human-level performance in data science competitions, 2024
Dominik Schmidt, Yuxiang Wu, and Zhengyao Jiang · 2024
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Litesearch: Efficacious tree search for llm, 2024
Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Dian Yu, Haitao Mi, Jinsong Su, and Dong Yu · 2024
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Mlcopilot: Unleashing the power of large language models in solving machine learning tasks, 2024
Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, and Yuqing Yang · 2024
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Language agent tree search unifies reasoning acting and planning in language models, 2024
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang · 2024
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