arXiv cs.AI· Cesare Barbera, Lorenzo Perini, Giovanni De Toni, Andrea Passerini, Andrea Pugnana·· 4 小时前AI 评分39
Divide et Calibra：通过 Vector Quantization 实现多分类局部校准（NeurIPS 2026 论文）
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
AI 导读
一篇被 NeurIPS 2026 接收的 arXiv 论文提出 Divide et Calibra，一种基于 Vector Quantization（VQ）的多分类局部校准方法：用 VQ 划分表示空间，并对 Dirichlet 浓度做索引化参数化，实现跨区域参数共享。
正文 · 原文
Abstract: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 representation-space dimensionality reduction, discarding latent directions relevant to miscalibration. To address these issues, we propose a compositional approach to multiclass calibration, where region-specific calibration maps are constructed from shared 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.
| Comments: | Accepted at the Fortieth Annual Conference on Neural Information Processing System (NeurIPS 2026) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| MSC classes: | 68T05 |
| Cite as: | arXiv:2605.21060 [cs.LG] |
| (or arXiv:2605.21060v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21060 arXiv-issued DOI via DataCite |
Submission history
From: Cesare Barbera [view email]
[v1]
Wed, 20 May 2026 11:44:55 UTC (3,612 KB)
[v2]
Mon, 26 Oct 2026 17:35:25 UTC (3,768 KB)
来源：arXiv cs.AI · arxiv.org