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English(EN) Robust Conformalized Selection with Noisy Responses

新研究应对在线共形预测和选择挑战 · 跟踪3个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

三篇新研究论文探讨了在线共形预测和选择的进展，解决了现有方法的局限性。第一篇论文介绍了一个统一的在线共形预测框架，即使在对抗性设置下也能同时控制覆盖率违规和预测集效率。第二篇论文侧重于有限反馈下的高效在线共形选择，在类似赌博机的反馈下证明了对抗有效性和随机效率。第三篇论文提出了鲁棒共形选择（RCS），一种旨在即使在校准数据被噪声响应污染时也能保持有效的错误发现率控制的方法，并在包括大型语言模型在内的各种应用中展示了其有效性。 AI

影响 这些论文推进了机器学习中不确定性量化的理论基础，有望提高LLM对齐和不确定性决策等应用的可靠性。

排序理由 该集群包含三篇在arXiv上发表的学术论文，详细介绍了机器学习算法的理论进展。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新研究应对在线共形预测和选择挑战 · 跟踪3个来源

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该集群包含三篇在arXiv上发表的学术论文，详细介绍了机器学习算法的理论进展。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Rahul Vaze ·

    在线一致性预测中的同步覆盖与效率保证

    arXiv:2607.26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations. First, their guarante…

  2. arXiv cs.LG TIER_1 English(EN) · Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala, Ali Sinop ·

    高效在线保形选择与有限反馈

    arXiv:2605.14953v2 Announce Type: replace Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identified with a pre-specified target probability $\phi$. While traditional online…

  3. arXiv stat.ML TIER_1 English(EN) · Chengyao Yu, Hongxin Wei, Bingyi Jing ·

    具有噪声响应的鲁棒共形选择

    arXiv:2607.22985v1 Announce Type: new Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models. N…

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