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The teaching size: computable teachers and learners for universal languages.

, , and . Mach. Learn., 108 (8-9): 1653-1675 (2019)

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Muppets: Multipurpose Table Segmentation., , , , and . IDA, volume 12695 of Lecture Notes in Computer Science, page 389-401. Springer, (2021)Robustness Testing of Machine Learning Families using Instance-Level IRT-Difficulty., , , and . EBeM@IJCAI, volume 3169 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Automated Data Transformation with Inductive Programming and Dynamic Background Knowledge., , , , , and . ECML/PKDD (3), volume 11908 of Lecture Notes in Computer Science, page 735-751. Springer, (2019)Probabilistic class hierarchies for multiclass classification., , and . J. Comput. Sci., (2018)Adapting Hierarchical Multiclass Classification to Changes in the Target Concept., , and . CAEPIA, volume 11160 of Lecture Notes in Computer Science, page 118-127. Springer, (2018)Fairness and Missing Values., , , and . CoRR, (2019)SALER: A Data Science Solution to Detect and Prevent Corruption in Public Administration., , , , and . Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, volume 11329 of Lecture Notes in Computer Science, page 103-117. Springer, (2018)Non-Cheating Teaching Revisited: A New Probabilistic Machine Teaching Model., , and . IJCAI, page 2973-2979. ijcai.org, (2022)Applying distances between terms to both flat and hierarchical data., , , and . AAIP, page 1-16. (2011)AUTOMATRIX: learning simple matrix pipelines., , and . Mach. Learn., 110 (4): 779-799 (2021)