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高维在线校准的调和权重算法

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研究给出针对任意凸集与误差范数的高维预测在线校准算法。对 d 个二元结果同时预测,该算法在固定精度下以 d^{O(1/ε)} 轮达到 ε-校准,指数级改进此前界;对多类预测同样达到 d^{O(1/ε)},优于 Peng 与 Fishelson 等人的 d^{\widetilde{O}(1/ε^2)}。

From arXiv · Statistics Machine Learning

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Oct 7, 2026
  1. arXiv · Statistics Machine Learning
    基于调和权重的高维在线校准

    研究给出针对任意凸集与误差范数的高维预测在线校准算法。对 d 个二元结果同时预测,该算法在固定精度下以 d^{O(1/ε)} 轮达到 ε-校准,指数级改进此前界;对多类预测同样达到 d^{O(1/ε)},优于 Peng 与 Fishelson 等人的 d^{\widetilde{O}(1/ε^2)}。

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