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
影响 Introduces a novel algorithmic approach for dynamic expert selection in machine learning, potentially improving efficiency in real-time decision-making systems.
排序理由 The cluster contains an arXiv preprint detailing a new algorithm for online Learning-to-Defer.
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