Follow
Ugo Tanielian
Ugo Tanielian
UPMC - Criteo
Verified email at criteo.com
Title
Cited by
Cited by
Year
Some theoretical properties of GANs
G Biau, B Cadre, M Sangnier, U Tanielian
562020
Distributionally robust counterfactual risk minimization
L Faury, U Tanielian, E Dohmatob, E Smirnova, F Vasile
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3850-3857, 2020
512020
Some theoretical insights into Wasserstein GANs
GÊ Biau, M Sangnier, U Tanielian
Journal of Machine Learning Research 22 (119), 1-45, 2021
482021
Learning disconnected manifolds: a no GANs land
U Tanielian, T Issenhuth, E Dohmatob, J Mary
Proceedings of the 37 th International Conference on Machine Learning …, 2020
382020
Approximating Lipschitz continuous functions with GroupSort neural networks
U Tanielian, G Biau
International Conference on Artificial Intelligence and Statistics, 442-450, 2021
352021
Edibert, a generative model for image editing
T Issenhuth, U Tanielian, J Mary, D Picard
arXiv preprint arXiv:2111.15264, 2021
122021
Relaxed softmax for PU learning
U Tanielian, F Vasile
Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019
122019
Siamese cookie embedding networks for cross-device user matching
U Tanielian, AM Tousch, F Vasile
Companion Proceedings of the The Web Conference 2018, 85-86, 2018
102018
Latent reweighting, an almost free improvement for GANs
T Issenhuth, U Tanielian, D Picard, J Mary
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
52022
Optimal 1-wasserstein distance for wgans
A Stéphanovitch, U Tanielian, B Cadre, N Klutchnikoff, G Biau
arXiv preprint arXiv:2201.02824, 2022
42022
Lessons from the AdKDD’21 privacy-preserving ML challenge
E Diemert, R Fabre, A Gilotte, F Jia, B Leparmentier, J Mary, Z Qu, ...
Proceedings of the ACM Web Conference 2022, 2026-2035, 2022
32022
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
V Shilova, LD Santos, F Vasile, G Racic, U Tanielian
arXiv preprint arXiv:2309.11507, 2023
22023
Unveiling the latent space geometry of push-forward generative models
T Issenhuth, U Tanielian, J Mary, D Picard
International Conference on Machine Learning, 14422-14444, 2023
22023
Wasserstein learning of determinantal point processes
L Anquetil, M Gartrell, A Rakotomamonjy, U Tanielian, C Calauzènes
arXiv preprint arXiv:2011.09712, 2020
22020
On the optimal precision of GANs
T Issenhuth, U Tanielian, J Mary, D Picard
12022
3DGEN: A GAN-based approach for generating novel 3D models from image data
A Schnepf, F Vasile, U Tanielian
arXiv preprint arXiv:2312.08094, 2023
2023
What Users Want? WARHOL: A Generative Model for Recommendation
J Samaran, U Tanielian, R Beaumont, F Vasile
Recommender Systems in Fashion and Retail: Proceedings of the Third Workshop …, 2022
2022
What Users Want? WARHOL: A Generative Model for Recommendation
UGO TANIELIAN, V FLAVIAN
arXiv preprint arXiv:2109.01093, 2021
2021
Generative Adversarial Networks: theory and practice
U Tanielian
Sorbonne Université, 2021
2021
«Generative Adversarial Networks»: théorie et pratique
U Tanielian
The system can't perform the operation now. Try again later.
Articles 1–20