11 papers
Conformal Prediction for Hierarchical Data
Guillaume Principato, Gilles Stoltz, Yvenn Amara-Ouali +3
We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others. Intuitively, the hierarchical struct…
Cascaded Transfer: Learning Many Tasks under Budget Constraints
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
In distributed applications, such as energy demand forecasting at the substation level or federated learning, a large number of related tasks must be learned by different models, w…
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
Yutong Wang, Yannig Goude, Qiwei Yao
We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain st…
Spatio-temporal modelling of electric vehicle charging demand
Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude +1
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; suc…
Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
Eloi Lindas, Yannig Goude, Philippe Ciais
In a growing renewable based energy system, accurate and reliable wind power forecasts are crucial for grid stability, balancing supply and demand and market risk management. Even…
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…