The Monge Gap: A Regularizer to Learn All Transport Maps. - Théo Uscidda; Marco Cuturi (ICML, 2023)

Generative Entropic Neural Optimal Transport To Map Within and Across Spaces. - Dominik Klein 005; Théo Uscidda; Fabian J. Theis; Marco Cuturi (CoRR, 2023)

Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation. - Luca Eyring; Dominik Klein 005; Théo Uscidda; Giovanni Palla; Niki Kilbertus; Zeynep Akata; Fabian J. Theis (ICLR, 2024)

Mirror and Preconditioned Gradient Descent in Wasserstein Space. - Clément Bonet; Théo Uscidda; Adam David; Pierre-Cyril Aubin-Frankowski; Anna Korba (NeurIPS, 2024)

GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics. - Dominik Klein 005; Théo Uscidda; Fabian J. Theis; Marco Cuturi (NeurIPS, 2024)

Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap. - Théo Uscidda; Luca Eyring; Karsten Roth; Fabian J. Theis; Zeynep Akata; Marco Cuturi (CoRR, 2024)

On the potential of Optimal Transport in Geospatial Data Science. - Nina Wiedemann; Théo Uscidda; Martin Raubal (CoRR, 2024)

Disentangled Representation Learning with the Gromov-Monge Gap. - Théo Uscidda; Luca Eyring; Karsten Roth; Fabian J. Theis; Zeynep Akata; Marco Cuturi (ICLR, 2025)