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English(EN) An Exponential Deterministic--Randomized Gap in ERM-Oracle Complexity for Thresholds on an Unknown Order

研究人员发现确定性与随机性方法在人工智能学习复杂度上存在指数级差距

研究人员在处理未知顺序的阈值时,发现了确定性和随机性方法在线学习复杂度上的指数级差距。AttiasHannekeRamaswamiNeurIPS 2025 上发表的一项研究表明,确定性学习者需要 T(或 T-epsilon)次预言机调用和错误,而随机性学习者两者都能达到对数界限。这种分离取决于一致性类型 ERM 预言机使用的特定规则,不同的规则会导致不同的性能结果。 AI

影响 强调了人工智能学习算法的理论局限性和潜在改进。

排序理由 详细介绍机器学习理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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研究人员发现确定性与随机性方法在人工智能学习复杂度上存在指数级差距

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详细介绍机器学习理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xuan Li ·

    ERM-Oracle 复杂度在未知阶阈值上的指数确定性-随机性差距

    arXiv:2609.10196v1 Announce Type: cross Abstract: Attias, Hanneke and Ramaswami (NeurIPS 2025) asked whether randomization provably reduces the oracle calls needed for online learning when the class is accessible only through an oracle. We study the instance they singled out: tra…