Adrien Pavao
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Citée par
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Judging competitions and benchmarks: a candidate election approach
A Pavao, M Vaccaro, I Guyon
ESANN 2021-29th European Symposium on Artificial Neural Networks, 2021
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
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, JCSJ Junior, M Madadi, ...
ICML Workshop 2020, 2020
Filtering participants improves generalization in competitions and benchmarks
A Pavao, Z Liu, I Guyon
ESANN 2022-European Symposium on Artificial Neural Networks, 2022
Hands-on tutorial on how to create your own challenge or benchmark
A Pavão
Special designs and competition protocols
WW Tu, A Pavão
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