72 citations · 81 across the 13 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
Yannis Montreuil, Letian Yu, Axel Carlier +2
Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adap…
cs.LG2025
Why Ask One When You Can Ask ? Learning-to-Defer to the Top- Experts
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
Existing Learning-to-Defer (L2D) frameworks are limited to single-expert deferral, forcing each query to rely on only one expert and preventing the use of collective expertise. We…