Seguir
Kimia Nadjahi
Kimia Nadjahi
CNRS - ENS Paris
Dirección de correo verificada de di.ens.fr - Página principal
Título
Citado por
Citado por
Año
Generalized sliced wasserstein distances
S Kolouri, K Nadjahi, U Simsekli, R Badeau, G Rohde
Advances in neural information processing systems 32, 2019
3072019
Statistical and topological properties of sliced probability divergences
K Nadjahi, A Durmus, L Chizat, S Kolouri, S Shahrampour, U Simsekli
Advances in Neural Information Processing Systems 33, 20802-20812, 2020
772020
Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance
K Nadjahi, A Durmus, U Simsekli, R Badeau
Advances in Neural Information Processing Systems 32, 2019
672019
Approximate Bayesian computation with the sliced-Wasserstein distance
K Nadjahi, V De Bortoli, A Durmus, R Badeau, U Şimşekli
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
382020
Safe policy improvement with soft baseline bootstrapping
K Nadjahi, R Laroche, R Tachet des Combes
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020
372020
Fast approximation of the sliced-Wasserstein distance using concentration of random projections
K Nadjahi, A Durmus, PE Jacob, R Badeau, U Simsekli
Advances in Neural Information Processing Systems 34, 12411-12424, 2021
332021
Generalized sliced probability metrics
S Kolouri, K Nadjahi, S Shahrampour, U Şimşekli
ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022
15*2022
Sliced-Wasserstein distance for large-scale machine learning: theory, methodology and extensions
K Nadjahi
Institut polytechnique de Paris, 2021
152021
Unbalanced optimal transport meets sliced-Wasserstein
T Séjourné, C Bonet, K Fatras, K Nadjahi, N Courty
arXiv preprint arXiv:2306.07176, 2023
92023
Shedding a PAC-Bayesian light on adaptive sliced-Wasserstein distances
R Ohana, K Nadjahi, A Rakotomamonjy, L Ralaivola
International Conference on Machine Learning, 26451-26473, 2023
52023
Asymmetry in low-rank adapters of foundation models
J Zhu, K Greenewald, K Nadjahi, HSO Borde, RB Gabrielsson, L Choshen, ...
arXiv preprint arXiv:2402.16842, 2024
32024
Federated wasserstein distance
A Rakotomamonjy, K Nadjahi, L Ralaivola
arXiv preprint arXiv:2310.01973, 2023
32023
Slicing Mutual Information Generalization Bounds for Neural Networks
K Nadjahi, K Greenewald, RB Gabrielsson, J Solomon
arXiv preprint arXiv:2406.04047, 2024
2024
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–13