Works & Publications
from our team
Works & Publications
from our team
Works & Publications
from our team
aspa-life (2024). aspa-life/Multimodal_Disentanglement. [online] GitHub. Available at: GitHub - aspa-life/Multimodal_Disentanglement.
Papers
Meunier, D., Li, Z., Gretton, A. and Kpotufe, S. (2024). Nonlinear Meta-Learning Can Guarantee Faster Rates. [online] arXiv.org. doi:https://doi.org/10.48550/arXiv.2307.10870.
Jordi Cortés-Andrés, Gustau Camps-Valls, Sippel, S., Eniko Melinda Székely, Sejdinovic, D., Emiliano Díaz, Adrián Pérez-Suay, Li, Z., Mahecha, M.D. and Reichstein, M. (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17(5), pp.054034–054034. doi:https://doi.org/10.1088/1748-9326/ac6762.
Li, Z., Perez-Suay, A., Gustau Camps-Valls and Sejdinovic, D. (2019). Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1911.04322.
Li, Z., Ton, J.-F., Oglic, D. and Sejdinovic, D. (2018). Towards A Unified Analysis of Random Fourier Features. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1806.09178.
aspa-life (2024). aspa-life/Multimodal_Disentanglement. [online] GitHub. Available at: GitHub - aspa-life/Multimodal_Disentanglement.
Papers
Meunier, D., Li, Z., Gretton, A. and Kpotufe, S. (2024). Nonlinear Meta-Learning Can Guarantee Faster Rates. [online] arXiv.org. doi:https://doi.org/10.48550/arXiv.2307.10870.
Jordi Cortés-Andrés, Gustau Camps-Valls, Sippel, S., Eniko Melinda Székely, Sejdinovic, D., Emiliano Díaz, Adrián Pérez-Suay, Li, Z., Mahecha, M.D. and Reichstein, M. (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17(5), pp.054034–054034. doi:https://doi.org/10.1088/1748-9326/ac6762.
Li, Z., Perez-Suay, A., Gustau Camps-Valls and Sejdinovic, D. (2019). Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1911.04322.
Li, Z., Ton, J.-F., Oglic, D. and Sejdinovic, D. (2018). Towards A Unified Analysis of Random Fourier Features. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1806.09178.
aspa-life (2024). aspa-life/Multimodal_Disentanglement. [online] GitHub. Available at: GitHub - aspa-life/Multimodal_Disentanglement.
Papers
Meunier, D., Li, Z., Gretton, A. and Kpotufe, S. (2024). Nonlinear Meta-Learning Can Guarantee Faster Rates. [online] arXiv.org. doi:https://doi.org/10.48550/arXiv.2307.10870.
Jordi Cortés-Andrés, Gustau Camps-Valls, Sippel, S., Eniko Melinda Székely, Sejdinovic, D., Emiliano Díaz, Adrián Pérez-Suay, Li, Z., Mahecha, M.D. and Reichstein, M. (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17(5), pp.054034–054034. doi:https://doi.org/10.1088/1748-9326/ac6762.
Li, Z., Perez-Suay, A., Gustau Camps-Valls and Sejdinovic, D. (2019). Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1911.04322.
Li, Z., Ton, J.-F., Oglic, D. and Sejdinovic, D. (2018). Towards A Unified Analysis of Random Fourier Features. arXiv (Cornell University). doi:https://doi.org/10.48550/arxiv.1806.09178.