共形预测遇上认知不确定性，新方法在预测区间上实现最优校准，UAI 2026收录的前沿研究。
arXiv:2505.19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by …
从再生核希尔伯特空间切入，用近似方法破解全共形预测的计算瓶颈，理论扎实且实用性强。
arXiv:2601.13102v3 Announce Type: replace-cross Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confid…
为受控马尔可夫链量身定制的模型化自助法，理论证明与实证兼备，统计推断和强化学习研究者值得一读。
arXiv:2605.12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled M…
把抽样设计的无偏性引入算法机器学习，为统计推断与模型训练架起新桥梁，值得技术党细读。
arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the idea…
利用任务可交换性理论，为合成数据提供统计推断有效性的新方法，解决隐私保护下的统计可靠性问题。
arXiv:2606.13629v1 Announce Type: cross Abstract: There is a proliferation of work arguing for the use of synthetic data in scientific research. For e…
探讨非交换面板数据的在线共形预测新方法，突破传统假设，提升实时预测可靠性
arXiv:2605.17705v1 Announce Type: cross Abstract: Panel data, in which multiple units are repeatedly observed over time, arise throughout science and …