Giulia Luise

According to our database, Giulia Luise authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bag of Policies for Distributional Deep Exploration.

Asen Nachkov, Luchen Li, Giulia Luise, Filippo Valdettaro, A. Aldo Faisal

Proceedings of the Epistemic Uncertainty in Artificial Intelligence, 2024

2023

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology.

Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein

Proceedings of the International Conference on Machine Learning, 2023

Meta Optimal Transport.

Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko

Proceedings of the International Conference on Machine Learning, 2023

2022

Schedule-Robust Online Continual Learning.

Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto

CoRR, 2022

Heterogeneous manifolds for curvature-aware graph embedding.

Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein

CoRR, 2022

2021

Entropic optimal transport in machine learning: applications to distributional regression, barycentric estimation and probability matching.

Giulia Luise

PhD thesis, 2021

Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition.

Paul Festor, Giulia Luise, Matthieu Komorowski, A. Aldo Faisal

CoRR, 2021

Aligning Time Series on Incomparable Spaces.

Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning.

Giulia Luise, Massimiliano Pontil, Carlo Ciliberto

CoRR, 2020

The Wasserstein Proximal Gradient Algorithm.

Adil Salim, Anna Korba, Giulia Luise

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning.

Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Non-Asymptotic Analysis for Stein Variational Gradient Descent.

Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm.

Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction.

Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance.

Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018