4 citations · 8 across the 9 of their papers we have counts for
10 papers
The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning
Theo Uscidda, Marta Tintore Gazulla, Maks Ovsjanikov +2
Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks. Existing approaches treat this gap as a kno…
Generalized Discrete Diffusion from Snapshots
Oussama Zekri, Théo Uscidda, Nicolas Boullé +1
We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete…
VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward
Zhaochong An, Orest Kupyn, Théo Uscidda +5
Large-scale video diffusion models achieve impressive visual quality, yet often fail to preserve geometric consistency. Prior approaches improve consistency either by augmenting th…
LATTS: Locally Adaptive Test-Time Scaling
Theo Uscidda, Matthew Trager, Michael Kleinman +3
One common strategy for improving the performance of Large Language Models (LLMs) on downstream tasks involves using a \emph{verifier model} to either select the best answer from a…
GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
Nina Wiedemann, Théo Uscidda, Martin Raubal
When predicting observations across space and time, the spatial layout of errors impacts a model's real-world utility. For instance, in bike sharing demand prediction, error patter…
Disentangled Representation Learning with the Gromov-Monge Gap
Théo Uscidda, Luca Eyring, Karsten Roth +3
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretab…
Mirror and Preconditioned Gradient Descent in Wasserstein Space
Clément Bonet, Théo Uscidda, Adam David +2
As the problem of minimizing functionals on the Wasserstein space encompasses many applications in machine learning, different optimization algorithms on have receiv…
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Luca Eyring, Dominik Klein, Théo Uscidda +4
In optimal transport (OT), a Monge map is known as a mapping that transports a source distribution to a target distribution in the most cost-efficient way. Recently, multiple neura…
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Dominik Klein, Théo Uscidda, Fabian Theis +1
Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing…
The Monge Gap: A Regularizer to Learn All Transport Maps
Théo Uscidda, Marco Cuturi
Optimal transport (OT) theory has been been used in machine learning to study and characterize maps that can push-forward efficiently a probability measure onto another. Recent wor…