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cs.LG2026
Discrete Wasserstein Flows for One-Step Generative Modeling
Alessandro Micheli, Andrea Zerio, Samir Bhatt
We introduce a new framework for one-step generative modelling on finite state spaces. To extend drifting beyond continuous domains, we use discrete Wasserstein geometry to define…
cs.LG2026
Riemannian Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
Computational optimal transport (OT) offers a principled framework for generative modeling. Neural OT methods, which use neural networks to learn an OT map (or potential) from data…
cs.LG2025
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Mélodie Monod, Alessandro Micheli, Samir Bhatt
We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification. Our non-parametric, architecture-agnostic framework captures time-varying…