Atelier RT-UQ "Deep Gaussian processes"

Dates et lieu

26 novembre 2026 à Sorbonne Université - Campus Pierre et Marie Curie dans l'Amphithéâtre Durand, 4 place Jussieu 75005 Paris.

Présentation

Cet atelier est coorganisé par le RT-UQ, l'université de Lille, l'université de Toulouse et l'IFP Energies Nouvelles. L'atelier consistera en un mini cours d'une heure et 6 présentations orales de 30 minutes (+15 minutes de discussions pour chaque). La langue utilisée sera l'anglais avec des slides en français ou anglais.

Organisateurs:
Agnès Lagnoux (IMT, UT2J), François Bachoc (Université de Lille) et Miguel Munoz Zuniga (IFPEN)

Sponsorisé par: RT-UQ and IMT


Orateurs confirmés

Mickael Binois (Inria Sophia Antipolis)
Franck Gabriel (Université Claude Bernard Lyon 1)
Ali Hebbal (Airbus)
Aretha L. Teckentrup (Université de Edimbourg)
Gianluca Finocchio (Université de Vienne)
Sébastien Marmin (LNE)
Oumar Baldé (CEA IRESNE)

Programme

9h - accueil

9h30 - 10h30. Mickael Binois.
Introduction to deep Gaussian processes (mini-cours).

10h30 - 11h pause café

11h - 11h45. Franck Gabriel.
(Neural tangent kernel, neural networks and further developpments)

11h45 - 12h30. Sébastien Marmin.
From warped Gaussian processes to deep functional priors: scalable Bayesian models for calibration and uncertainty quantification.

12h30 - 14h pause déjeuner (au frais des participants)

14h - 14h45. Aretha L Teckentrup.

14h45 - 15h30. Gianluca Finocchio.
Posterior Contraction for Deep Gaussian Process Priors.

15h30 - 16h pause café

16h - 16h45. Ali Hebbal.
Geometry-Aware Deep Gaussian Process.

16h45 - 17h30 Oumar Baldé


Résumés

Gianluca Finocchio. Posterior Contraction for Deep Gaussian Process Priors.
We study posterior contraction rates for a class of deep Gaussian process priors in the nonparametric regression setting under a general composition assumption on the regression function. It is shown that the contraction rates can achieve the minimax convergence rate (up to log n factors), while being adaptive to the underlying structure and smoothness of the target function. The proposed framework extends the Bayesian nonparametric theory for Gaussian process priors.

Mickael Binois. Introduction to deep Gaussian processes.
After a brief summary on the limitations of standard Gaussian processes for non-stationary response modeling, we will present an overview of the various frameworks for building deep Gaussian processes (DGP). They will be compared in terms of inference and predictions tools, as well as on the specific effects of the number of layers. Pointers on active learning strategies based on DGPs will also be provided. To complement this methodological and theoretical side, illustrative examples will be proposed in Python or R.

Ali Hebbal. Geometry-Aware Deep Gaussian Process.
Learning solutions to physical problems governed by partial differential equations (PDEs) is a critical task in engineering, yet variability in the underlying geometry of the domain often complicates it. Traditional surrogate modeling techniques struggle when faced with nonparametrized geometrical changes, such as varying mesh structures, node counts, and topologies between samples. This presentation introduces the Geometry-Aware Deep Gaussian Process (GA-DGP), a novel architecture designed to address this challenge. The GA-DGP leverages Deep Gaussian Processes (DGPs) to create a hierarchical representation of the problem. We train the entire model end-to-end by maximizing an Evidence Lower Bound (ELBO). We demonstrate the effectiveness and competitiveness of our approach on a benchmark of different physics, showing that GA-DGP achieves state-of-the-art performance while retaining the uncertainty quantification benefits inherent to Bayesian methods.

Sébastien Marmin. From warped GP to deep functional priors: scalable Bayesian models for calibration and UQ
In this talk, I will discuss a progression from non-stationary and warped Gaussian processes to deep probabilistic models, with Bayesian calibration as an application. I will highlight how random feature approximations and variational inference make these models computationally tractable. More broadly, I will discuss connections with Bayesian neural networks and recent approaches that aim to retain explicit control over functional priors in deep models.

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