Giulia Luise
Giulia Luise
Microsoft Research
Verified email at - Homepage
Cited by
Cited by
Differential properties of sinkhorn approximation for learning with wasserstein distance
G Luise, A Rudi, M Pontil, C Ciliberto
Advances in Neural Information Processing Systems 31, 2018
Sinkhorn barycenters with free support via frank-wolfe algorithm
G Luise, S Salzo, M Pontil, C Ciliberto
Advances in neural information processing systems 32, 2019
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, 4672-4682, 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, 15360-15370, 2020
The wasserstein proximal gradient algorithm
A Salim, A Korba, G Luise
Advances in Neural Information Processing Systems 33, 12356-12366, 2020
Generalization properties of optimal transport GANs with latent distribution learning
G Luise, M Pontil, C Ciliberto
arXiv preprint arXiv:2007.14641, 2020
Heterogeneous manifolds for curvature-aware graph embedding
F Di Giovanni, G Luise, M Bronstein
arXiv preprint arXiv:2202.01185, 2022
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
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
Contraction and regularizing properties of heat flows in metric measure spaces
G Luise, G Savaré
arXiv preprint arXiv:1904.09825, 2019
Meta optimal transport
B Amos, S Cohen, G Luise, I Redko
arXiv preprint arXiv:2206.05262, 2022
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
P Festor, G Luise, M Komorowski, AA Faisal
arXiv preprint arXiv:2109.07827, 2021
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
F Di Giovanni, L Giusti, F Barbero, G Luise, P Lio, M Bronstein
arXiv preprint arXiv:2302.02941, 2023
Schedule-Robust Online Continual Learning
R Wang, M Ciccone, G Luise, M Pontil, A Yapp, C Ciliberto
arXiv preprint arXiv:2210.05561, 2022
Entropic Optimal Transport in Machine Learning: applications to distributional regression, barycentric estimation and probability matching
G Luise
UCL (University College London), 2021
Detecting Spatiotemporal Lightning Patterns: An Unsupervised Graph-Based Approach
E Benjaminson, S Praveen, G Luise, JE Johnson, R Strange, ...
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Articles 1–16