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New online algorithm enhances Learning-to-Defer with dynamic experts

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new online algorithm for Learning-to-Defer (L2D) methods, designed to handle streaming data and dynamic expert availability. This algorithm is the first of its kind for multiclass classification with bandit feedback and a varying pool of experts. It offers theoretical regret guarantees and has demonstrated effectiveness in experiments on both synthetic and real-world datasets, extending L2D capabilities to more complex, dynamic environments. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a novel algorithmic approach for dynamic expert selection in machine learning, potentially improving efficiency in real-time decision-making systems.

RANK_REASON The cluster contains an arXiv preprint detailing a new algorithm for online Learning-to-Defer.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Dang Hoang Duy, Yannis Montreuil, Maxime Meyer, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi ·

    Online Learning-to-Defer with Varying Experts

    arXiv:2605.12340v1 Announce Type: new Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing exper…

  2. arXiv stat.ML TIER_1 · Wei Tsang Ooi ·

    Online Learning-to-Defer with Varying Experts

    Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert availability, and shifting expert distribution…

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