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AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

, , , , , , , , , , и . WorkingPaper, (13.06.2023)
DOI: 10.48550/arXiv.2306.08107

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Claudia Tornede

Heinrich Tornede

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Alexander Stadler

Messtechnische Bestimmung und Simulation der Kernverluste in weichmagnetischen Materialien. Uni Erlangen-Nürnberg, (2009)

Alexander Auch

A phylogenetic potpourri: computational methods for analysing genome scale data. Uni Tübingen, (2010)
 

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Extreme Algorithm Selection with Dyadic Feature Representation., , и . DS, том 12323 из Lecture Notes in Computer Science, стр. 309-324. Springer, (2020)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information., , , , , и . Trans. Mach. Learn. Res., (2023)Configuration and Evaluation, , , , , , и . (22.06.2023)Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning., , , и . IEEE Trans. Pattern Anal. Mach. Intell., 43 (9): 3055-3066 (2021)Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis., , , , и . ACML, том 129 из Proceedings of Machine Learning Research, стр. 737-752. PMLR, (2020)Automated Machine Learning, Bounded Rationality, and Rational Metareasoning., , , и . CoRR, (2021)AutoML for Multi-Label Classification: Overview and Empirical Evaluation., , , и . IEEE Trans. Pattern Anal. Mach. Intell., 43 (9): 3037-3054 (2021)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information, , , , , и . WorkingPaper, (02.12.2022)Towards Green Automated Machine Learning: Status Quo and Future Directions., , , , , и . J. Artif. Intell. Res., (2023)Hybrid Ranking and Regression for Algorithm Selection., , , и . KI, том 12325 из Lecture Notes in Computer Science, стр. 59-72. Springer, (2020)