| 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 |
| Infinite task learning in RKHSs R Brault, A Lambert, Z Szabó, M Sangnier, F d’Alché-Buc The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 15 | 2019 |
| Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses A Lambert, D Bouche, Z Szabo, F d’Alché-Buc International Conference on Machine Learning, 11844-11867, 2022 | 8 | 2022 |
| Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nystr\" om method Q Tao, F Tonin, A Lambert, Y Chen, P Patrinos, JAK Suykens arXiv preprint arXiv:2406.08748, 2024 | 2 | 2024 |
| Emotion Transfer Using Vector-Valued Infinite Task Learning A Lambert, S Parekh, Z Szabó, F d'Alché-Buc arXiv preprint arXiv:2102.05075, 2021 | 2 | 2021 |
| Extending kernel PCA through dualization: sparsity, robustness and fast algorithms F Tonin, A Lambert, P Patrinos, J Suykens International Conference on Machine Learning, 34379-34393, 2023 | 1 | 2023 |
| Continuous emotion transfer using kernels A Lambert, S Parekh, Z Szabo, F d'Alché-Buc | | 2021 |
| Learning function-valued functions in reproducible kernel Hilbert spaces with integral losses: Application to infinite task learning A Lambert Institut Polytechnique de Paris, 2021 | | 2021 |
| A Functional Extension of Multi-Output Learning A Lambert, R Brault, Z Szabo, F d’Alché-Buc | | |
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
