3 papers
cs.LG2026
CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun +1
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We pro…
cs.CL2024
Combining Objective and Subjective Perspectives for Political News Understanding
Evan Dufraisse, Adrian Popescu, Julien Tourille +2
Researchers and practitioners interested in computational politics rely on automatic content analysis tools to make sense of the large amount of political texts available on the We…
cs.SI2023
All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media
Celina Treuillier, Sylvain Castagnos, Armelle Brun
Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strateg…