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English(EN) The Sample Complexity of Multicalibration

新研究确立了在线多校准的最优下界

作者 PulseAugur 编辑部 · [2 个来源] ·

arXiv上发表的两篇新论文探讨了机器学习中多校准的理论基础。第一篇论文确立了在线多校准的紧确下界，证明了与边际校准在信息论上的分离。第二篇论文研究了批量设置中多校准的样本复杂度，证明了要达到一定的误差容忍度，需要 $\widetilde{\Theta}(\varepsilon^{-3})$ 个样本，且该数量是充分必要的。 AI

影响 这些理论发现可能通过阐明校准的基本限制，为开发更鲁棒和公平的机器学习模型提供信息。

排序理由 该集群包含两篇发表在arXiv上的学术论文，涉及机器学习校准的理论方面。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新研究确立了在线多校准的最优下界

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Natalie Collina, Jiuyao Lu, Georgy Noarov, Aaron Roth ·

    Online Multicalibration 的最优下界

    arXiv:2601.05245v2 Announce Type: replace-cross Abstract: We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner's…

  2. arXiv stat.ML TIER_1 English(EN) · Aaron Roth ·

    多校准的样本复杂度

    We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i.i.d. samples from an unknown distribution and must output a (possibly randomized) predictor whose population multicalibration error, measured by Expected Calibration Error (E…

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