The monge gap: A regularizer to learn all transport maps T Uscidda, M Cuturi International Conference on Machine Learning, 34709-34733, 2023 | 30 | 2023 |
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation L Eyring*, D Klein, T Uscidda*, G Palla, N Kilbertus, Z Akata, F Theis The Twelveth International Conference on Learning Representations, 2023 | 8 | 2023 |
Mirror and Preconditioned Gradient Descent in Wasserstein Space C Bonet, T Uscidda, A David, PC Aubin-Frankowski, A Korba The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 6 | 2024 |
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics D Klein*, T Uscidda*, FJ Theis The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | 6* | |
On the potential of Optimal Transport in geospatial data science N Wiedemann, T Uscidda, M Raubal The Twelveth International Conference on Learning Representations Workshop …, 2024 | | 2024 |
Disentangled Representation Learning with the Gromov-Monge Gap T Uscidda*, L Eyring*, K Roth, F Theis, Z Akata, M Cuturi The Thirteenth International Conference on Learning Representations, 2024 | | 2024 |