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 . This summer I took part in the research sprint with Frontier Development Lab , an applied AI research accelerator that works in partnership with NASA.
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, B. Amos, 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
A. Korba, A. Salim, M. Arbel, GL, A. Gretton A Non-Asymptotic Analysis for Stein Variational Gradient Descent , 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.