通过分布鲁棒优化学习可信集成，更好捕捉数据分布变化下的不确定性。
Learning Credal Ensembles via Distributionally Robust Optimization
- 基于训练与测试数据分布假设差异，构建可信模型集合。
- 在多个基准上实现更优的分布外检测与医疗选择性分类表现。
- 适合关注模型鲁棒性与不确定性量化的研究者与工程师。
可信预测器是能够感知认知不确定性并输出概率预测凸集的模型，能合理量化预测的认知不确定性（EU），并在多种场景中提升模型鲁棒性。然而，当前主流方法将EU定义为随机初始化导致的模型分歧，主要反映优化随机性敏感度，而非深层来源的不确定性。为此，我们提出将EU定义为在训练与测试数据间不同独立同分布（i.i.d.）假设松弛下训练出的模型之间的分歧。基于此，我们提出CreDRO，通过分布鲁棒优化学习一组合理的模型集成。结果表明，CreDRO不仅捕捉了训练随机性带来的不确定性，还捕获了因训练-测试分布偏移导致的有意义分歧。实验证明，其在多个基准上的分布外检测任务及医疗场景的选择性分类任务中持续优于现有可信方法。
原文摘要 · Abstract (English)
Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epistemic uncertainty (EU) and have been shown to improve model robustness in various settings. However, most state-of-the-art methods mainly define EU as disagreement caused by random training initializations, which mostly reflects sensitivity to optimization randomness rather than uncertainty from deeper sources. To address this, we define EU as disagreement among models trained with varying relaxations of the i.i.d. assumption between training and test data. Based on this idea, we propose CreDRO, which learns an ensemble of plausible models through distributionally robust optimization. As a result, CreDRO captures EU not only from training randomness but also from meaningful disagreement due to potential distribution shifts between training and test data. Empirical results show that CreDRO consistently outperforms existing credal methods on tasks such as out-of-distribution detection across multiple benchmarks and selective classification in medical applications.
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。