| 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 | 117 | 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 | 67 | 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 | 61 | 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 | 39 | 2020 |
| The Wasserstein proximal gradient algorithm A Salim, A Korba, G Luise Advances in Neural Information Processing Systems 33, 12356-12366, 2020 | 36 | 2020 |
| 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, MM Bronstein International Conference on Machine Learning, 7865-7885, 2023 | 28 | 2023 |
| Generalization properties of optimal transport GANs with latent distribution learning G Luise, M Pontil, C Ciliberto arXiv preprint arXiv:2007.14641, 2020 | 20 | 2020 |
| 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 | 17 | 2021 |
| Heterogeneous manifolds for curvature-aware graph embedding F Di Giovanni, G Luise, M Bronstein arXiv preprint arXiv:2202.01185, 2022 | 16 | 2022 |
| Meta optimal transport B Amos, S Cohen, G Luise, I Redko arXiv preprint arXiv:2206.05262, 2022 | 10 | 2022 |
| 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 | 10 | 2019 |
| Contraction and regularizing properties of heat flows in metric measure spaces G Luise, G Savaré arXiv preprint arXiv:1904.09825, 2019 | 10 | 2019 |
| ’, and Michael Bronstein. 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 Proceedings of the 40th International Conference on Machine Learning, 2023 | 8 | 2023 |
| 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 | 8 | 2021 |
| Schedule-robust online continual learning R Wang, M Ciccone, G Luise, A Yapp, M Pontil, C Ciliberto arXiv preprint arXiv:2210.05561, 2022 | 1 | 2022 |
| Bag of Policies for Distributional Deep Exploration A Nachkov, L Li, G Luise, F Valdettaro, A Faisal arXiv preprint arXiv:2308.01759, 2023 | | 2023 |
| Entropic Optimal Transport in Machine Learning: applications to distributional regression, barycentric estimation and probability matching G Luise UCL (University College London), 2021 | | 2021 |
| Detecting Spatiotemporal Lightning Patterns: An Unsupervised Graph-Based Approach E Benjaminson, S Praveen, G Luise, JE Johnson, R Strange, ... | | |
Carlo CilibertoUniversity College LondonVerified email at ucl.ac.uk
