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Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

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M. Feurer, K. Eggensperger, E. Bergman, F. Pfisterer, B. Bischl, and F. Hutter · 2020

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H2O AutoML: Scalable automatic machine learning

Erin LeDell and Sebastien Poirier · 2020

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On hyperparameter optimization of machine learning algorithms: Theory and practice

L. Yang and A. Shami · 2020

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Competition-level Code Generation with AlphaCode

Y. Li and et al · 2022

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AutoKeras: An AutoML Library for Deep Learning

H. Jin, F. Chollet, Q. Song, and X. Hu · 2023

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Gpt-4 technical report

OpenAI · 2023

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Anthropic · 2024

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MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

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J. S. Chan, N. Chowdhury, O. Jaffe, J. Aung, D. Sherburn, E. Mays, G. Starace, K. Liu, L. Maksin, T. Patwardhan, L. Weng, and A. Mądry · 2024

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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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Google · 2024

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Mlagentbench: Evaluating language agents on machine learning experimentation

Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2024

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SWE-bench: Can language models resolve real-world github issues?

Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan · 2024

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