Giulia Luise
Title
Cited by
Cited by
Year
Differential properties of sinkhorn approximation for learning with wasserstein distance
G Luise, A Rudi, M Pontil, C Ciliberto
arXiv preprint arXiv:1805.11897, 2018
552018
Sinkhorn barycenters with free support via frank-wolfe algorithm
G Luise, S Salzo, M Pontil, C Ciliberto
arXiv preprint arXiv:1905.13194, 2019
242019
Leveraging low-rank relations between surrogate tasks in structured prediction
G Luise, D Stamos, M Pontil, C Ciliberto
International Conference on Machine Learning, 4193-4202, 2019
52019
Contraction and regularizing properties of heat flows in metric measure spaces
G Luise, G Savaré
arXiv preprint arXiv:1904.09825, 2019
52019
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
A Korba, A Salim, M Arbel, G Luise, A Gretton
Advances in Neural Information Processing Systems 33, 2020
42020
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
G Luise, M Pontil, C Ciliberto
arXiv preprint arXiv:2007.14641, 2020
32020
Aligning Time Series on Incomparable Spaces
S Cohen, G Luise, A Terenin, B Amos, M Deisenroth
International Conference on Artificial Intelligence and Statistics, 1036-1044, 2021
2021
Entropic Optimal Transport in Machine Learning: applications to distributional regression, barycentric estimation and probability matching
G Luise
UCL (University College London), 2021
2021
The Wasserstein Proximal Gradient Algorithm
A Salim, A Korba, G Luise
arXiv e-prints, arXiv: 2002.03035, 2020
2020
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
L Oneto, M Donini, G Luise, C Ciliberto, A Maurer, M Pontil
Advances in Neural Information Processing Systems 33, 2020
2020
The Wasserstein Proximal Gradient Algorithm Open Website
A Salim, A Korba, G Luise
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