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Extreme Algorithm Selection with Dyadic Feature Representation., , and . DS, volume 12323 of Lecture Notes in Computer Science, page 309-324. Springer, (2020)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information., , , , , and . Trans. Mach. Learn. Res., (2023)Configuration and Evaluation, , , , , , and . (Jun 22, 2023)Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning., , , and . IEEE Trans. Pattern Anal. Mach. Intell., 43 (9): 3055-3066 (2021)Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis., , , , and . ACML, volume 129 of Proceedings of Machine Learning Research, page 737-752. PMLR, (2020)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information, , , , , and . WorkingPaper, (Dec 2, 2022)Hybrid Ranking and Regression for Algorithm Selection., , , and . KI, volume 12325 of Lecture Notes in Computer Science, page 59-72. Springer, (2020)Towards Green Automated Machine Learning: Status Quo and Future Directions., , , , , and . J. Artif. Intell. Res., (2023)Automated Machine Learning, Bounded Rationality, and Rational Metareasoning., , , and . CoRR, (2021)AutoML for Multi-Label Classification: Overview and Empirical Evaluation., , , and . IEEE Trans. Pattern Anal. Mach. Intell., 43 (9): 3037-3054 (2021)