1st Early-Career Researchers’ Day of the DATA Programme I-SITE CAP 20-25

Session

Foundations and Sustainability in Machine Learning

26 févr. 2026, 14:20

Amphi 2 (Pôle Commun)

Présidents de session

Foundations and Sustainability in Machine Learning

• Andrés Duque (PhD Student)

Foundations and Sustainability in Machine Learning

• Paul Stos (Université Clermont-Auvergne)

13. Apport des motifs gradual dans pour l'IA explicable

26/02/2026 14:20

Contribution orale

Les modèles ensemblistes et les réseaux de neurones profonds démontrent de très bons résultats dans les tâches de classification. Cependant, leur nature « boîte noire » empêche leur déploiement généralisé dans des domaines critiques comme la santé. L'IA explicable vise à rendre ces modèles plus compréhensibles. Dans la littérature, les résultats de classification sont expliqués principalement...

22. Fluctuations and concentration in two-layer neural networks

26/02/2026 14:40

Contribution orale

We study the learning dynamics of wide two-layer neural networks trained by stochastic gradient descent (SGD), aiming to understand quantitatively how network width shapes both the typical training trajectory and the variability of the final predictor.

We adopt an interacting particle viewpoint in which neurons evolve under SGD as a large coupled system. As the width grows, this...

9. Contributing to reproducible software measurements of energy consumption for machine learning

26/02/2026 15:00

Contribution orale

This work is part of the AI domain of the DATA program, and more specifically about sensors to obtain the energy consumption and machine learning algorithms. This work is being carried out in the LIMOS laboratory.
High-performance computing require careful management of power and energy budgets. Much of the work to achieve these energy-consumption goals will have to be done through hardware...

7. Towards Sustainable DBMS: A Framework for Real-Time Energy Estimation and Query Categorization

26/02/2026 15:40

Contribution orale

Abstract. Energy efficiency in database management systems (DBMS) is increasingly critical due to the rising computational demands of modern applica-tions. Our work proposes a complete framework to analyze energy consumption. We developed a real-time monitoring framework that captures CPU and memory utilization during query execution and estimates energy consumption. We have implemented a...