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English(EN) Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts

新方法增强共形预测以实现鲁棒的不确定性量化

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新的研究论文探讨了共形回归和预测的进展。第一篇论文介绍了 CLAPS，一种结合了学习到的输入相关噪声和最后一层认知不确定性的方法，以提高回归任务中的区间效率。第二篇论文提出了裁剪最小二乘重要性拟合 (CLISF)，以解决在处理无界协变量偏移时加权共形预测的欠覆盖问题，并提供鲁棒性的理论保证。 AI

影响 这些论文推动了机器学习模型中不确定性量化的发展，有望在关键应用中实现更可靠的预测。

排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了共形回归和预测的新颖方法。

在 arXiv cs.LG 阅读 →

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新方法增强共形预测以实现鲁棒的不确定性量化

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两篇在 arXiv 上发表的学术论文，详细介绍了共形回归和预测的新颖方法。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Sacha Braun, Eug\`ene Berta, Michael I. Jordan, Francis Bach ·

    用于共形预测的多变量标准化残差

    arXiv:2507.20941v4 Announce Type: replace-cross Abstract: While split conformal prediction guarantees marginal coverage, approaching the stronger property of conditional coverage is essential for reliable uncertainty quantification. Naive conformal scores, however, suffer from po…

  2. arXiv cs.LG TIER_1 English(EN) · Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh ·

    CLAPS: 通过最后一层拉普拉斯进行共形回归的随机-认知不确定性度量

    arXiv:2512.01384v4 Announce Type: replace Abstract: Conformal regression provides finite-sample marginal coverage, but it does not by itself determine how interval width should adapt across heterogeneous inputs. Existing locally adaptive methods mainly account for aleatoric noise…

  3. arXiv cs.LG TIER_1 English(EN) · James Wang, Surbhi Goel ·

    无界协变量偏移下的鲁棒一致性推理的权重裁剪

    arXiv:2605.02072v1 Announce Type: new Abstract: Conformal prediction (CP) provides powerful, distribution-free prediction sets, but its guarantees rely on the exchangeability of training and test data, which is often violated in practice due to covariate shifts. While weighted co…

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