做高可靠性机器学习模型（如医疗、金融）的团队，终于有了一个能兼顾全局和局部校准的实用方法——VQ分区加参数共享的设计让校准更精准，建议做模型可靠性的开发者点开看看具体实现。
该论文提出了一种基于向量量化（VQ）的多类校准方法，解决了全局校准假设误差均匀分布和局部校准因降维导致信息丢失的问题。方法通过VQ对表示空间进行结构化分区，并利用共享码字依赖因子构建区域特定的校准映射，同时引入狄利克雷浓度的索引参数化实现跨区域参数共享。实验表明，该方法在保持全局校准和预测性能的同时，显著提升了局部校准效果，尤其适用于稀疏区域。
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume calibration errors are homogeneous across the latent space, while local methods often rely on latent-space dimensionality reduction, which leads to information loss. To address these issues, we propose a compositional approach to multiclass calibration, where region-specific calibration maps are constructed from shared codeword-dependent factors. We instantiate this idea via Vector Quantization (VQ), which induces a structured partition of the representation space, and an indexed parameterization of Dirichlet concentrations that enables parameter sharing across regions. Our approach learns heterogeneous calibration maps that generalize well even to sparse regions of the latent space. Experiments on benchmark datasets show significant improvements in local calibration while maintaining competitive global calibration and predictive performance.