72 citations · 81 across the 13 of their papers we have counts for
8 papers · 1 filter
A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
An inaccurate expert can still provide useful information after correction. We study online learning to defer in which the learner chooses an expert and fixes a correction function…
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…
Consistent Learning-to-Defer with Expert-Conditional Advice
Yannis Montreuil, Leïna Montreuil, Axel Carlier +2
We introduce learning-to-defer with advice, a new setting in which a system learns both which expert should handle an input and which information that expert should receive. The po…
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…
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…