| Codalab competitions: An open source platform to organize scientific challenges A Pavao, I Guyon, AC Letournel, DT Tran, X Baro, HJ Escalante, ... Journal of Machine Learning Research 24 (198), 1-6, 2023 | 137 | 2023 |
| Generation and evaluation of privacy preserving synthetic health data A Yale, S Dash, R Dutta, I Guyon, A Pavao, KP Bennett Neurocomputing 416, 244-255, 2020 | 120 | 2020 |
| Privacy preserving synthetic health data A Yale, S Dash, R Dutta, I Guyon, A Pavao, KP Bennett ESANN 2019-European Symposium on Artificial Neural Networks, Computational …, 2019 | 56 | 2019 |
| Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Z Xu, S Escalera, A Pavao, M Richard, WW Tu, Q Yao, H Zhao, I Guyon Patterns 3 (7), 2022 | 33 | 2022 |
| Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 Z Liu, A Pavao, Z Xu, S Escalera, F Ferreira, I Guyon, S Hong, F Hutter, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3108-3125, 2021 | 32 | 2021 |
| Assessing privacy and quality of synthetic health data A Yale, S Dash, R Dutta, I Guyon, A Pavao, KP Bennett Proceedings of the Conference on Artificial Intelligence for Data Discovery …, 2019 | 32 | 2019 |
| Towards automated deep learning: Analysis of the autodl challenge series 2019 Z Liu, Z Xu, S Rajaa, M Madadi, JCSJ Junior, S Escalera, A Pavao, ... NeurIPS 2019 Competition and Demonstration Track, 242-252, 2020 | 24 | 2020 |
| Synthetic event time series health data generation S Dash, R Dutta, I Guyon, A Pavao, A Yale, KP Bennett arXiv preprint arXiv:1911.06411, 2019 | 16 | 2019 |
| Autocv challenge design and baseline results Z Liu, I Guyon, JJ Junior, M Madadi, S Escalera, A Pavao, HJ Escalante, ... CAp 2019-Conférence sur l'Apprentissage Automatique, 2019 | 16 | 2019 |
| Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design G Serré, E Boguslawski, B Donnot, A Pavão, I Guyon, A Marot arXiv preprint arXiv:2207.10330, 2022 | 9 | 2022 |
| Towards automated computer vision: analysis of the AutoCV challenges 2019 Z Liu, Z Xu, S Escalera, I Guyon, JCSJ Júnior, M Madadi, A Pavao, ... Pattern Recognition Letters 135, 196-203, 2020 | 9 | 2020 |
| Autodl challenge design and beta tests-towards automatic deep learning Z Liu, O Bousquet, A Elisseeff, S Escalera, I Guyon, J Jacques, A Pavao, ... MetaLearn workshop@ NeurIPS2018, 2018 | 7 | 2018 |
| Design and analysis of experiments: A challenge approach in teaching A Pavao, D Kalainathan, L Sun-Hosoya, K Bennett, I Guyon NeurIPS 2019-33th Annual Conference on Neural Information Processing Systems, 2019 | 5 | 2019 |
| Codabench: Flexible, easy-to-use and reproducible benchmarking for everyone Z Xu, H Zhao, WW Tu, M Richard, S Escalera, I Guyon arXiv preprint arXiv 2110, 2021 | 4 | 2021 |
| Aircraft numerical “twin”: A time series regression competition A Pavao, I Guyon, N Stéphane, F Lebeau, M Ghienne, L Platon, ... 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 3 | 2021 |
| Judging competitions and benchmarks: a candidate election approach A Pavao, M Vaccaro, I Guyon ESANN 2021-29th European Symposium on Artificial Neural Networks, 2021 | 3 | 2021 |
| Filtering participants improves generalization in competitions and benchmarks A Pavao, Z Liu, I Guyon ESANN 2022-European Symposium on Artificial Neural Networks, 2022 | 2 | 2022 |
| How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge Z Liu, A Pavao, Z Xu, S Escalera, I Guyon, JCJ Junior, M Madadi, ... ICML Workshop 2020, 2020 | 2 | 2020 |
| Methodology for Design and Analysis of Machine Learning Competitions A Pavão Université Paris-Saclay, 2023 | 1 | 2023 |
| Hands-on tutorial on how to create your own challenge or benchmark A Pavão | | 2024 |
