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Hybrid Ranking and Regression for Algorithm Selection.

, , , and . KI, volume 12325 of Lecture Notes in Computer Science, page 59-72. Springer, (2020)

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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)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)Configuration and Evaluation, , , , , , and . (Jun 22, 2023)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information, , , , , and . Transactions on Machine Learning Research, (Apr 18, 2023)Position: A Call to Action for a Human-Centered AutoML Paradigm., , , , , , , , and . CoRR, (2024)Towards Meta-Algorithm Selection., , and . CoRR, (2020)HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection, , and . NeurIPS Workshop on Meta Learning (MetaLearn 2022), (2022)Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data and Simplifying Meta Level Decisions.. University of Paderborn, Germany, (2023)Symbolic Explanations for Hyperparameter Optimization., , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 2/1-22. PMLR, (2023)Towards Green Automated Machine Learning: Status Quo and Future Directions., , , , , and . J. Artif. Intell. Res., (2023)