2 papers
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…
stat.ML2025
Adversarial Robustness in One-Stage Learning-to-Defer
Yannis Montreuil, Letian Yu, Axel Carlier +2
Learning-to-Defer (L2D) enables hybrid decision-making by routing inputs either to a predictor or to external experts. While promising, L2D is highly vulnerable to adversarial pert…