提出无需重训练的不确定性量化方法，可生成预测区间与完整分布。
Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
- 基于上下文归一化流，后置到已训练模型上
- 预测区间校准良好，性能媲美顶尖方法
- 适合高风险场景下的决策支持
深度回归模型中的不确定性量化对理解模型置信度及高风险领域的安全决策至关重要。现有方法生成的预测区间忽略了分布信息，未能考虑多模态或非对称分布对决策的影响。而全贝叶斯或近似贝叶斯方法虽能获得预测后验密度，却需大幅修改模型结构并重新训练。本文提出MCNF，一种新型后置式不确定性量化方法，可在不重训练模型的前提下，同时生成预测区间和完整的条件预测分布。实验表明，基于MCNF的不确定性估计具有良好的校准性，性能与当前最优方法相当，并为下游决策任务提供更丰富的信息。
原文摘要 · Abstract (English)
Quantifying uncertainty in deep regression models is important both for understanding the confidence of the model and for safe decision-making in high-risk domains. Existing approaches that yield prediction intervals overlook distributional information, neglecting the effect of multimodal or asymmetric distributions on decision-making. Similarly, full or approximated Bayesian methods, while yielding the predictive posterior density, demand major modifications to the model architecture and retraining. We introduce MCNF, a novel post hoc uncertainty quantification method that produces both prediction intervals and the full conditioned predictive distribution. MCNF operates on top of the underlying trained predictive model; thus, no predictive model retraining is needed. We provide experimental evidence that the MCNF-based uncertainty estimate is well calibrated, is competitive with state-of-the-art uncertainty quantification methods, and provides richer information for downstream decision-making tasks.
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