I recently finished my PhD in Machine Learning at UCL , London. I worked under the supervision of Massimiliano Pontil and Carlo Ciliberto. In November 2020, I started a PostDoc at the Department of Computing at Imperial College .
My main research interest focuses on the interplay of Machine Learning and Optimal Transport, in particular on using optimal transport distances for learning probability measures in supervised or unsupervised settings. I also work a bit on structured prediction and surrogate frameworks. Recently, I started working on more applied topics -targeting Reinforcement Learning for healthcare.
You can find my full CV here .
GL, M. Pontil, C. Ciliberto, Generalization Properties of Optimal Transport GANs with Latent Distribution Learning , submitted
S. Cohen, GL , A. Terenin, A. Brandon, M. Deisenroth, Aligning Time Series on Incomparable Spaces , AISTATS 2021
A. Salim, A. Korba, GL, Wasserstein Proximal Gradient , NeurIPS 2020
L. Oneto, M. Donini, GL, C. Ciliberto, A. Maurer and M. Pontil, Exploiting MMD and Sinkhorn Divergences for Learning Fair and Transferable Representations , NeurIPS 2020
GL, S. Salzo, M. Pontil, C.Ciliberto, Sinkhorn Barycenter with Free Support via Frank-Wolfe Algorithm , NeurIPS 2019 (spotlight)
GL, G. Savare’, Contraction and regularizing properties of heat flows in metric measure spaces , DCDS-Series S, doi: 10.3934/dcdss.2020327
GL, D. Stamos, M. Pontil, C. Ciliberto, Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction, ICML 2019.
GL, A. Rudi, M. Pontil, C. Ciliberto, Differential Properties of Sinkhorn Approximations for Learning with Wasserstein Loss , NeurIPS 2018.