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2020 – today
- 2024
[c10]Asen Nachkov, Luchen Li
, Giulia Luise, Filippo Valdettaro, A. Aldo Faisal:
Bag of Policies for Distributional Deep Exploration. Epi UAI 2024: 28-43- 2023
- [c9]Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko:
Meta Optimal Transport. ICML 2023: 791-813 - [c8]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. ICML 2023: 7865-7885 - [i12]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. CoRR abs/2302.02941 (2023) - [i11]Asen Nachkov, Luchen Li, Giulia Luise, Filippo Valdettaro, Aldo A. Faisal:
Bag of Policies for Distributional Deep Exploration. CoRR abs/2308.01759 (2023) - 2022
- [i10]Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein:
Heterogeneous manifolds for curvature-aware graph embedding. CoRR abs/2202.01185 (2022) - [i9]Brandon Amos, Samuel Cohen, Giulia Luise, Ievgen Redko:
Meta Optimal Transport. CoRR abs/2206.05262 (2022) - [i8]Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto:
Schedule-Robust Online Continual Learning. CoRR abs/2210.05561 (2022) - 2021
- [b1]Giulia Luise:
Entropic optimal transport in machine learning: applications to distributional regression, barycentric estimation and probability matching. University College London, UK, 2021 - [c7]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. AISTATS 2021: 1036-1044 - [i7]Paul Festor, Giulia Luise, Matthieu Komorowski, A. Aldo Faisal:
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition. CoRR abs/2109.07827 (2021) - 2020
- [c6]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS 2020 - [c5]Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil:
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. NeurIPS 2020 - [c4]Adil Salim, Anna Korba, Giulia Luise:
The Wasserstein Proximal Gradient Algorithm. NeurIPS 2020 - [i6]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR abs/2006.09797 (2020) - [i5]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. CoRR abs/2006.12648 (2020) - [i4]Giulia Luise, Massimiliano Pontil, Carlo Ciliberto:
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. CoRR abs/2007.14641 (2020)
2010 – 2019
- 2019
- [c3]Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto:
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. ICML 2019: 4193-4202 - [c2]Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto:
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. NeurIPS 2019: 9318-9329 - [i3]Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto:
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. CoRR abs/1903.00667 (2019) - [i2]Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto:
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. CoRR abs/1905.13194 (2019) - 2018
- [c1]Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto:
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. NeurIPS 2018: 5864-5874 - [i1]Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto:
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. CoRR abs/1805.11897 (2018)
Coauthor Index
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