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Filtering participants improves generalization in competitions and benchmarks., , and . ESANN, (2022)Methodology for Design and Analysis of Machine Learning Competitions. (Méthodologie pour la conception et l'analyse de compétitions en apprentissage automatique).. University of Paris-Saclay, France, (2023)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, , , , , , , , , and 15 other author(s). HAL, (September 2020)Judging competitions and benchmarks: a candidate election approach., , and . ESANN, (2021)Assessing privacy and quality of synthetic health data., , , , , and . AIDR, page 8:1-8:4. ACM, (2019)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, , , , , , , , , and 15 other author(s). IEEE Transactions on Pattern Analysis and Machine Intelligence, (2021)To appear.Privacy Preserving Synthetic Health Data., , , , , and . ESANN, (2019)Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019., , , , , , , , , and . NeurIPS (Competition and Demos), volume 123 of Proceedings of Machine Learning Research, page 242-252. PMLR, (2019)Aircraft Numerical "Twin": A Time Series Regression Competition., , , , , , , , , and 21 other author(s). ICMLA, page 441-448. IEEE, (2021)