3 papers
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…
stat.ML2025
Diffusion Models for Inverse Problems in the Exponential Family
Alessandro Micheli, Mélodie Monod, Samir Bhatt
Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting thei…