Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. - Giulia Luise; Alessandro Rudi; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2018)

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. - Giulia Luise; Dimitrios Stamos; Massimiliano Pontil; Carlo Ciliberto (ICML, 2019)

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. - Giulia Luise; Saverio Salzo; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2019)

A Non-Asymptotic Analysis for Stein Variational Gradient Descent. - Anna Korba; Adil Salim; Michael Arbel; Giulia Luise; Arthur Gretton (NeurIPS, 2020)

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. - Luca Oneto; Michele Donini; Giulia Luise; Carlo Ciliberto; Andreas Maurer; Massimiliano Pontil (NeurIPS, 2020)

The Wasserstein Proximal Gradient Algorithm. - Adil Salim; Anna Korba; Giulia Luise (NeurIPS, 2020)

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. - Giulia Luise; Massimiliano Pontil; Carlo Ciliberto (CoRR, 2020)

Aligning Time Series on Incomparable Spaces. - Samuel Cohen; Giulia Luise; Alexander Terenin; Brandon Amos; Marc Peter Deisenroth (AISTATS, 2021)

Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition. - Paul Festor; Giulia Luise; Matthieu Komorowski; A. Aldo Faisal (CoRR, 2021)

Entropic optimal transport in machine learning: applications to distributional regression, barycentric estimation and probability matching. - Giulia Luise (2021)

Heterogeneous manifolds for curvature-aware graph embedding. - Francesco Di Giovanni; Giulia Luise; Michael M. Bronstein (CoRR, 2022)

Schedule-Robust Online Continual Learning. - Ruohan Wang; Marco Ciccone; Giulia Luise; Andrew Yapp; Massimiliano Pontil; Carlo Ciliberto (CoRR, 2022)

Meta Optimal Transport. - Brandon Amos; Giulia Luise; Samuel Cohen; Ievgen Redko (ICML, 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 (ICML, 2023)

Bag of Policies for Distributional Deep Exploration. - Asen Nachkov; Luchen Li; Giulia Luise; Filippo Valdettaro; A. Aldo Faisal (Epi UAI, 2024)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)