| 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 | 32 | 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 | 30 | 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 | 23* | 2020 |
| 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 | 15* | 2022 |
| On Medians of (Randomized) Pairwise Means P Laforgue, S Clémençon, P Bertail International Conference on Machine Learning, 1272-1281, 2019 | 15 | 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 | 14* | 2021 |
| Statistical learning from biased training samples S Clémençon, P Laforgue Electronic Journal of Statistics 16 (2), 6086-6134, 2022 | 10 | 2022 |
| Fast Kernel Methods for Generic Lipschitz Losses via -Sparsified Sketches TE Ahmad, P Laforgue, F d'Alché-Buc arXiv preprint arXiv:2206.03827, 2022 | 8* | 2022 |
| Multitask online mirror descent N Cesa-Bianchi, P Laforgue, A Paudice, M Pontil arXiv preprint arXiv:2106.02393, 2021 | 7 | 2021 |
| Sketch in, sketch out: Accelerating both learning and inference for structured prediction with kernels T El Ahmad, L Brogat-Motte, P Laforgue, F d’Alché-Buc International Conference on Artificial Intelligence and Statistics, 109-117, 2024 | 5 | 2024 |
| Linear bandits with memory: from rotting to rising G Clerici, P Laforgue, N Cesa-Bianchi arXiv preprint arXiv:2302.08345, 2023 | 4 | 2023 |
| Multitask learning with no regret: from improved confidence bounds to active learning PG Sessa, P Laforgue, N Cesa-Bianchi, A Krause Advances in Neural Information Processing Systems 36, 6770-6781, 2023 | 1 | 2023 |
| Fighting selection bias in statistical learning: application to visual recognition from biased image databases S Clémençon, P Laforgue, R Vogel Journal of Nonparametric Statistics, 1-24, 2023 | 1 | 2023 |
| Deep Sketched Output Kernel Regression for Structured Prediction TE Ahmad, J Yang, P Laforgue, F d'Alché-Buc arXiv preprint arXiv:2406.09253, 2024 | | 2024 |
| Multitask Online Learning: Listen to the Neighborhood Buzz J Achddou, N Cesa-Bianchi, P Laforgue arXiv preprint arXiv:2310.17385, 2023 | | 2023 |
| Deep kernel representation learning for complex data and reliability issues P Laforgue Institut Polytechnique de Paris, 2020 | | 2020 |
| Linear Bandits with Memory G Clerici, P Laforgue, N Cesa-Bianchi | | |
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