Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B Khalil, and Deepak S Turaga. Learning feature engineering for classification. In Ijcai, volume 17, pp. 2529-2535, 2017.
OpenAI. Gpt-4 technical report, 2023a.
OpenAI. openai/openai-cookbook: Examples and guides for using the openai api. https://github.com/openai/openai-cookbook, 2023b. (Accessed on 04/20/2023).
OpenAI Community. GPT-3 can't count syllables - or doesn't "get" haiku. https://community.openai.com/t/gpt-3-cant-count-syllables-or-doesnt-get-haiku/18733, 2021. Accessed on: 2023-03-21.
Vinay Uday Prabhu and Abeba Birhane. Large image datasets: A pyrrhic win for computer vision?, 2020.
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna. Ai and the everything in the whole wide world benchmark, 2021.
Chris Rohlf. Ai code generation and cybersecurity, April 2023. URL https://www.cfr.org/blog/ai-code-generation-and-cybersecurity.
Alaina N. Talboy and Elizabeth Fuller. Challenging the appearance of machine intelligence: Cognitive bias in Ilms and best practices for adoption, 2023.
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo. Openml: Networked science in machine learning. SIGKDD Explorations, 15(2):49-60, 2013. doi: 10.1145/2641190.2641198. URL http://doi.acm.org/10.1145/2641190.2641198.
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luís Torgo. Openml: networked science in machine learning. CoRR, abs/1407.7722, 2014. URL http://arxiv.org/abs/1407.7722.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. URL http://arxiv.org/abs/1706.03762.
David Vos, Till Dohmen, and Sebastian Schelter. Towards parameter-efficient automation of data wrangling tasks with prefix-tuning. In NeurIPS 2022 First Table Representation Workshop, 2022. URL https://openreview.net/forum?id=8kyYJs2YkFH.
Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, and Frank Hutter. Can fairness be automated? guidelines and opportunities for fairness-aware automl, 2023.
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652, 2021.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2023.
Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman (eds.), Proceedings of the 9th Python in Science Conference, pp. 56 - 61, 2010. doi: 10.25080/Majora-92bf1922-00a.
Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. *Chemometrics and intelligent laboratory systems*, 2(1-3):37-52, 1987.
Jianyu Zhang, Jianye Hao, Françoise Fogelman-Soulie, and Zan Wang. Automatic feature engineering by deep reinforcement learning. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 2312-2314, 2019.
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019.