When ot meets mom: Robust estimation of wasserstein distance G Staerman, P Laforgue, P Mozharovskyi, F d’Alché-Buc
International Conference on Artificial Intelligence and Statistics, 136-144, 2021
20 2021 Autoencoding any data through kernel autoencoders P Laforgue, S Clémençon, F d’Alché-Buc
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
16 2019 Duality in RKHSs with infinite dimensional outputs: Application to robust losses P Laforgue, A Lambert, L Brogat-Motte, F d’Alché-Buc
International Conference on Machine Learning, 5598-5607, 2020
14 * 2020 On Medians of (Randomized) Pairwise Means P Laforgue, S Clémençon, P Bertail
International Conference on Machine Learning, 1272-1281, 2019
10 2019 Generalization bounds in the presence of outliers: a median-of-means study P Laforgue, G Staerman, S Clémençon
International Conference on Machine Learning, 5937-5947, 2021
8 * 2021 Statistical Learning from Biased Training Samples P Laforgue, S Clémençon
arXiv preprint arXiv:1906.12304, 2019
8 * 2019 A last switch dependent analysis of satiation and seasonality in bandits P Laforgue, G Clerici, N Cesa-Bianchi, R Gilad-Bachrach
International Conference on Artificial Intelligence and Statistics, 971-990, 2022
3 * 2022 Multitask online mirror descent N Cesa-Bianchi, P Laforgue, A Paudice, M Pontil
arXiv preprint arXiv:2106.02393, 2021
3 2021 AdaTask: Adaptive Multitask Online Learning P Laforgue, A Della Vecchia, N Cesa-Bianchi, L Rosasco
arXiv preprint arXiv:2205.15802, 2022
1 2022 TE Ahmad, P Laforgue, F d'Alché-Buc
arXiv preprint arXiv:2206.03827, 2022
2022 Statistical learning from biased training samples S Clémençon, P Laforgue
Electronic Journal of Statistics 16 (2), 6086-6134, 2022
2022 Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases S Clémençon, P Laforgue, R Vogel
arXiv preprint arXiv:2109.02357, 2021
2021 Visual Recognition with Deep Learning from Biased Image Datasets R Vogel, S Clémençon, P Laforgue
arXiv e-prints, arXiv: 2109.02357, 2021
2021 Deep kernel representation learning for complex data and reliability issues P Laforgue
Institut polytechnique de Paris, 2020
2020 
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisAdresse e-mail validée de telecom-paris.fr
