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Clément Calauzènes
Clément Calauzènes
Criteo AI Lab
Verified email at criteo.com
Title
Cited by
Cited by
Year
Offline a/b testing for recommender systems
A Gilotte, C Calauzènes, T Nedelec, A Abraham, S Dollé
Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018
2272018
Fairness-aware learning for continuous attributes and treatments
J Mary, C Calauzenes, N El Karoui
International Conference on Machine Learning, 4382-4391, 2019
1232019
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
T Issenhuth, J Mary, C Calauzènes
European Conference on Computer Vision (ECCV), 2020
982020
Improved Optimistic Algorithms for Logistic Bandits
L Faury, M Abeille, C Calauzènes, O Fercoq
International Conference on Machine Learning, 2020
832020
On the (non-) existence of convex, calibrated surrogate losses for ranking
C Calauzenes, N Usunier, P Gallinari
Advances in Neural Information Processing Systems 25, 2012
592012
Learning scoring functions with order-preserving losses and standardized supervision
D Buffoni, C Calauzenes, P Gallinari, N Usunier
The 28th International Conference on Machine Learning (ICML 2011), 825-832, 2011
392011
Learning in repeated auctions
T Nedelec, C Calauzènes, N El Karoui, V Perchet
Foundations and Trends® in Machine Learning 15 (3), 176-334, 2022
372022
Instance-wise minimax-optimal algorithms for logistic bandits
M Abeille, L Faury, C Calauzènes
International Conference on Artificial Intelligence and Statistics, 3691-3699, 2021
362021
End-to-end learning of geometric deformations of feature maps for virtual try-on
T Issenhuth, J Mary, C Calauzènes
arXiv preprint arXiv:1906.01347, 2019
252019
Bridging the gap between regret minimization and best arm identification, with application to a/b tests
R Degenne, T Nedelec, C Calauzènes, V Perchet
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
212019
Jointly efficient and optimal algorithms for logistic bandits
L Faury, M Abeille, KS Jun, C Calauzènes
International Conference on Artificial Intelligence and Statistics, 546-580, 2022
162022
Distributed SAGA: Maintaining linear convergence rate with limited communication
C Calauzenes, NL Roux
arXiv preprint arXiv:1705.10405, 2017
122017
Regret bounds for generalized linear bandits under parameter drift
L Faury, Y Russac, M Abeille, C Calauzènes
arXiv preprint arXiv:2103.05750, 2021
112021
Explicit shading strategies for repeated truthful auctions
M Abeille, C Calauzènes, NE Karoui, T Nedelec, V Perchet
arXiv preprint arXiv:1805.00256, 2018
92018
Pure exploration and regret minimization in matching bandits
F Sentenac, J Yi, C Calauzenes, V Perchet, M Vojnovic
International Conference on Machine Learning, 9434-9442, 2021
82021
Real-Time Optimisation for Online Learning in Auctions
L Croissant, M Abeille, C Calauzenes
International Conference on Machine Learning, 2020
82020
Calibration and regret bounds for order-preserving surrogate losses in learning to rank
C Calauzènes, N Usunier, P Gallinari
Machine learning 93, 227-260, 2013
72013
Improving evolutionary strategies with generative neural networks
L Faury, C Calauzenes, O Fercoq, S Krichen
arXiv preprint arXiv:1901.11271, 2019
62019
Thresholding the virtual value: a simple method to increase welfare and lower reserve prices in online auction systems
T Nedelec, M Abeille, C Calauzènes, N El Karoui, B Heymann, V Perchet
arXiv preprint arXiv:1808.06979, 2018
62018
On ranking via sorting by estimated expected utility
C Calauzènes, N Usunier
Advances in Neural Information Processing Systems 33, 3699-3710, 2020
52020
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