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Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning.
Scikit-Learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Édouard Duchesnay · 2011
Earlier work this paper cites.
Taking human out of learning applications: A survey on automated machine learning
Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., and Yu, Y · 2018
Earlier work this paper cites.
Building Machine Learning and Deep Learning Models on Google Cloud Platform
Bisong, E · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Earlier work this paper cites.
Auto-Keras: An efficient neural architecture search system
Jin, H., Song, Q., and Hu, X · 2019
Earlier work this paper cites.
Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions
Mukunthu, D., Shah, P., and Tok, W · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
AutoGluon-Tabular: Robust and accurate AutoML for structured data
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A · 2020
Earlier work this paper cites.
A comprehensive survey of neural architecture search: Challenges and solutions
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Chen, X., and Wang, X · 2020
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AutoML: A survey of the state-of-the-art
He, X., Zhao, K., and Chu, X · 2021
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LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Earlier work this paper cites.
AutoML to date and beyond: Challenges and opportunities
Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., and Veeramachaneni, K · 2021
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Neural Network Intelligence, 1 2021
Microsoft · 2021
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Generating a machine learning model with a few sentences
Trirat, P., Shin, Y., Kim, S., and Kim, M · 2021
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Auto-Sklearn 2.0: Hands-free AutoML via meta-learning
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F · 2022
Earlier work this paper cites.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Benchmarking large language models as AI research agents
Huang, Q., Vora, J., Liang, P., and Leskovec, J · 2023
Cited alongside, same era.
Decomposed Prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., and Sabharwal, A · 2023
Cited alongside, same era.
PaperQA: Retrieval-augmented generative agent for scientific research
Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G., and White, A. D · 2023
Cited alongside, same era.
TrainerAgent: Customizable and efficient model training through LLM-powered multi-agent system
Li, H., Jiang, H., Zhang, T., Yu, Z., Yin, A., Cheng, H., Fu, S., Zhang, Y., and He, W · 2023
Cited alongside, same era.
Self-Refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., and Clark, P · 2023
Large language models orchestrating structured reasoning achieve Kaggle grandmaster level
Grosnit, A., Maraval, A., Doran, J., Paolo, G., Thomas, A., Beevi, R. S. H. N., Gonzalez, J., Khandelwal, K., Iacobacci, I., Benechehab, A., et al · 2024
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Understanding the planning of LLM agents: A survey
Huang, X., Liu, W., Chen, X., Wang, X., Wang, H., Lian, D., Wang, Y., Tang, R., and Chen, E · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Exploring large language models for feature selection: A data-centric perspective
Li, D., Tan, Z., and Liu, H · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
AutoGluon–TimeSeries: AutoML for probabilistic time series forecasting
Shchur, O., Turkmen, A. C., Erickson, N., Shen, H., Shirkov, A., Hu, T., and Wang, B · 2023
Cited alongside, same era.
HuggingGPT: Solving AI tasks with ChatGPT and its friends in Hugging Face
Shen, Y., Song, K., Tan, X., Li, D., Lu, W., and Zhuang, Y · 2023
Cited alongside, same era.
AutoML in the wild: Obstacles, workarounds, and expectations
Sun, Y., Song, Q., Gui, X., Ma, F., and Wang, T · 2023
Cited alongside, same era.
Prompt2model: Generating deployable models from natural language instructions
Viswanathan, V., Zhao, C., Bertsch, A., Wu, T., and Neubig, G · 2023
Cited alongside, same era.
Graph neural networks are inherently good generalizers: Insights by bridging GNNs and MLPs
Yang, C., Wu, Q., Wang, J., and Yan, J · 2023
Cited alongside, same era.
AutoML-GPT: Automatic machine learning with GPT
Zhang, S., Gong, C., Wu, L., Liu, X., and Zhou, M · 2023
Cited alongside, same era.
SELA: Tree-search enhanced LLM agents for automated machine learning
Chi, Y., Lin, Y., Hong, S., Pan, D., Fei, Y., Mei, G., Liu, B., Pang, T., Kwok, J., Zhang, C., et al · 2024
Cited alongside, same era.
Malberg, S., Mosca, E., and Groh, G · 2024
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The landscape of emerging AI agent architectures for reasoning, planning, and tool calling: A survey
Masterman, T., Besen, S., Sawtell, M., and Chao, A · 2024
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Aviary: training language agents on challenging scientific tasks
Narayanan, S., Braza, J. D., Griffiths, R.-R., Ponnapati, M., Bou, A., Laurent, J., Kabeli, O., Wellawatte, G., Cox, S., Rodriques, S. G., et al · 2024
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AutoGluon-Multimodal (AutoMM): Supercharging multimodal AutoML with foundation models
Tang, Z., Fang, H., Zhou, S., Yang, T., Zhong, Z., Hu, T., Kirchhoff, K., and Karypis, G · 2024
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AutoML in the age of large language models: Current challenges, future opportunities and risks
Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., and Lindauer, M · 2024
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WizardLM: Empowering large pre-trained language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., Lin, Q., and Jiang, D · 2024
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ResearchAgent: Iterative research idea generation over scientific literature with large language models
Baek, J., Jauhar, S. K., Cucerzan, S., and Hwang, S. J · 2025
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Curie: Toward rigorous and automated scientific experimentation with AI agents
Kon, P. T. J., Liu, J., Ding, Q., Qiu, Y., Yang, Z., Huang, Y., Srinivasa, J., Lee, M., Chowdhury, M., and Chen, A · 2025
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AgentHPO: Large language model agent for hyper-parameter optimization
Liu, S., Gao, C., and Li, Y · 2025
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AutoAgent: A fully-automated and zero-code framework for LLM agents, 2025
Tang, J., Fan, T., and Huang, C · 2025
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The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al · 2025
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AutoMMLab: Automatically generating deployable models from language instructions for computer vision tasks
Yang, Z., Zeng, W., Jin, S., Qian, C., Luo, P., and Liu, W · 2025
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