Sergio Calvo Ordoñez — accepted AI-conference papers

2026

Richer Bayesian Last Layers with Subsampled NTK Features

Sergio Calvo Ordoñez, Jonathan Plenk, Richard Bergna, Alvaro Cartea, Yarin Gal, Jose Miguel Hernandez-Lobato +1

ICML 2026poster

Bayesian last layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underestimate epistemic uncertainty because they apply a Bayesian treatment only to the final layer, ignoring uncertainty induced by earlier layers. We propose…

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2025

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations

Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka, Pietro Lio, José Miguel Hernández-Lobato

ICLR 2025spotlight

We propose a novel Stochastic Differential Equation (SDE) framework to address the problem of learning uncertainty-aware representations for graph-structured data. While Graph Neural Ordinary Differential Equations (GNODEs) have shown promise in learning node representations, they lack the ability t…

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2024

Partially Stochastic Infinitely Deep Bayesian Neural Networks

Sergio Calvo Ordoñez, Matthieu Meunier, Francesco Piatti, YUANTAO SHI

ICML 2024poster

In this paper, we present Partially Stochastic Infinitely Deep Bayesian Neural Networks, a novel family of architectures that integrates partial stochasticity into the framework of infinitely deep neural networks. Our new class of architectures is designed to improve the computational efficiency of…

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