AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity.
CoRR, 2024
Methodology for Design and Analysis of Machine Learning Competitions. (Méthodologie pour la conception et l'analyse de compétitions en apprentissage automatique).
PhD thesis, 2023
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges.
J. Mach. Learn. Res., 2023
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.
Patterns, 2022
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design.
CoRR, 2022
Filtering participants improves generalization in competitions and benchmarks.
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.
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IEEE Trans. Pattern Anal. Mach. Intell., 2021
Aircraft Numerical "Twin": A Time Series Regression Competition.
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Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021
Judging competitions and benchmarks: a candidate election approach.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021
Towards automated computer vision: analysis of the AutoCV challenges 2019.
Pattern Recognit. Lett., 2020
Generation and evaluation of privacy preserving synthetic health data.
Neurocomputing, 2020
Synthetic Event Time Series Health Data Generation.
CoRR, 2019
Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019.
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019
Privacy Preserving Synthetic Health Data.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019
Assessing privacy and quality of synthetic health data.
Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse, 2019