7 citations · 8 across the 17 of their papers we have counts for
6 papers · 1 filter
Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
A learning-to-defer (L2D) system decides, for each input, whether to predict on its own or to hand it to one of several available experts. The very well established recipe trains c…
Online Learning-to-Defer with Varying Experts
Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3
Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. Real-world deployments require handling streaming data, changing expert availa…
Learning-to-Defer with Expert-Conditional Advice
Yannis Montreuil, Leïna Montreuil, Axel Carlier +2
Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed at decision time. Many modern…
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…
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…
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
Yannis Montreuil, Shu Heng Yeo, Axel Carlier +2
The Two-Stage Learning-to-Defer (L2D) framework has been extensively studied for classification and, more recently, regression tasks. However, many real-world applications require…