提出新方法选核密度带宽，让模型不确定性评估更准。
Bandwidth Selection in Kernel Density Estimation for Model Calibration
- 用风险对齐思想优化核密度带宽选择
- 在多个数据集上显著降低校准误差
- 适合需要可靠不确定性的高风险场景
随着深度学习模型在高风险应用中日益普及，提供准确的不确定性估计与高预测精度同样关键。虽然核密度估计（KDE）作为传统分箱法的平滑替代，能有效量化校准偏差，但其可靠性高度依赖于核带宽的选择。标准方法如最大似然估计（MLE）常无法为校准任务生成最优带宽。本文提出风险对齐（Risk Alignment, RA），一种新的优化框架，通过使KDE重构的风险与经验风险对齐来确定最优带宽。理论证明该对齐可最小化整个数据分布上的校准估计偏差，建立适用于多种度量的标准准则，包括极具挑战性的标准校准误差。在多种架构和数据集上的大量实验表明，RA始终优于标准带宽选择方法，带来更可靠的校准评估。
原文摘要 · Abstract (English)
As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as achieving high predictive accuracy. While Kernel Density Estimation (KDE) has emerged as a smooth and continuous alternative to traditional binning for quantifying miscalibration, its reliability is heavily dependent on the choice of the kernel bandwidth. Standard selection techniques, such as Maximum Likelihood Estimation (MLE), often fail to produce optimal bandwidths for calibration tasks. In this work, we introduce Risk Alignment (RA), a novel optimization framework that determines the optimal bandwidth by aligning KDE-reconstructed risk with empirical risk. We theoretically demonstrate that this alignment minimizes calibration estimation bias across the data distribution, establishing a principled bandwidth selection criterion applicable to various metrics, including the challenging case of canonical calibration error. Extensive experiments across multiple architectures and datasets show that RA consistently outperforms standard bandwidth selection methods, yielding more reliable calibration assessments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。