CRUDE：经验校准回归不确定性分布
CRUDE: Calibrating Regression Uncertainty Distributions Empirically
摘要：
机器学习中校准的不确定性估计对自动驾驶汽车、医学以及天气和气候预测等许多领域至关重要。虽然有大量的文献不确定性校准分类，分类结果并不总是转化为回归。因此，在回归设置中预测不确定性的现代模型通常会产生未经校准和过度自信的估计。为了解决这些差距，我们提出了一种回归设置的校准方法，该方法不假设误差上的特定不确定性分布：经验校准回归不确定性分布（CRUDE）。CRUDE假设误差分布在整个输出空间中具有恒定的任意形状，由预测的平均值移动，并由预测的标准差缩放。我们详细介绍了CRUDE和共形推理之间的理论联系。在一系列广泛的回归任务中，CRUDE始终表现出比最先进的技术更清晰，更好的校准和更准确的不确定性估计。
英文摘要：
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting. While there is extensive literature on uncertainty calibration for classification, the classification findings do not always translate to regression. As a result, modern models for predicting uncertainty in regression settings typically produce uncalibrated and overconfident estimates. To address these gaps, we present a calibration method for regression settings that does not assume a particular uncertainty distribution over the error: Calibrating Regression Uncertainty Distributions Empirically (CRUDE). CRUDE makes the weaker assumption that error distributions have a constant arbitrary shape across the output space, shifted by predicted mean and scaled by predicted standard deviation. We detail a theoretical connection between CRUDE and conformal inference. Across an extensive set of regression tasks, CRUDE demonstrates consistently sharper, better calibrated, and more accurate uncertainty estimates than state-of-the-art techniques.
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