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New framework boosts adversarial robustness in one-stage learning-to-defer systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to enhance the adversarial robustness of one-stage learning-to-defer (L2D) systems. This approach addresses vulnerabilities in L2D models, which can be manipulated by adversarial perturbations to alter both predictions and deferral decisions. The proposed method includes formalizing attacks, introducing cost-sensitive adversarial surrogate losses, and providing theoretical guarantees for classification and regression tasks. Experiments demonstrate improved robustness against various attacks while maintaining performance on clean data. AI

IMPACT Introduces a new method to secure hybrid decision-making systems against adversarial attacks, potentially improving reliability in critical applications.

RANK_REASON The cluster contains a new academic paper detailing a novel framework for adversarial robustness in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework boosts adversarial robustness in one-stage learning-to-defer systems

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The cluster contains a new academic paper detailing a novel framework for adversarial robustness in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yannis Montreuil, Letian Yu, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi ·

    Adversarial Robustness in One-Stage Learning-to-Defer

    arXiv:2510.10988v2 Announce Type: replace Abstract: 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 perturbations, which can not only flip predictions b…

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