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Statistical Comparison of Algorithm Performance Through Instance Selection.

, , , , and . CP, volume 210 of LIPIcs, page 43:1-43:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2021)

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Challenges of Acquiring Compositional Inductive Biases via Meta-Learning., , and . Meta-Knowledge Transfer @ ECML/PKDD, volume 191 of Proceedings of Machine Learning Research, page 11-23. PMLR, (2022)Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling., and . PPSN (1), volume 12269 of Lecture Notes in Computer Science, page 95-110. Springer, (2020)A typed applicative system for a language and text processing engineering., , , and . J. Innov. Digit. Ecosyst., 1 (1-2): 26-37 (2014)Text Processing Chains: Getting Help from Typed Applicative Systems., and . FLAIRS, page 579-584. AAAI Press, (2016)Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML., , , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 10/1-34. PMLR, (2023)Integration of Sequence of Computational Modules Dedicated to Text Analysis: A Combinatory Typed Approach., , , and . FLAIRS, AAAI Press, (2013)Statistical Comparison of Algorithm Performance Through Instance Selection., , , , and . CP, volume 210 of LIPIcs, page 43:1-43:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2021)Opening the Black Box: Automated Software Analysis for Algorithm Selection., , , and . AutoML, volume 188 of Proceedings of Machine Learning Research, page 6/1-18. PMLR, (2022)Greybox Algorithm Configuration.. IJCAI, page 4875-4876. ijcai.org, (2021)Combining sequential model-based algorithm configuration with default-guided probabilistic sampling., and . GECCO Companion, page 301-302. ACM, (2020)