72 citations · 81 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
One-Stage Top- Learning-to-Defer: Score-Based Surrogates with Theoretical Guarantees
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
We introduce the first one-stage Top- Learning-to-Defer framework, which unifies prediction and deferral by learning a shared score-based model that selects the most cost-ef…
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…
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
Two-stage Learning-to-Defer (L2D) enables optimal task delegation by assigning each input to either a fixed main model or one of several offline experts, supporting reliable decisi…