鲁棒验证：即使分布偏移也能给出可信预测
Robust Validation: Confident Predictions Even When Distributions Shift
AI总结：
本文提出一种基于共形推断的鲁棒预测推断方法，能在 f-散度球内的任意测试分布偏移下保持预测集的有效覆盖，并通过估计未来数据偏移量构建鲁棒性，在多个大规模基准数据集上验证了其有效性。
摘要：
尽管机器学习和统计学的传统观点假设训练样本和测试样本来自同一总体，但实践与这一假设相悖。因此，一种来自鲁棒统计与优化的策略是构建对分布扰动具有鲁棒性的模型。在本文中，我们采用一种不同的方法来描述鲁棒预测推断的程序，其中模型对其预测提供不确定性估计，而不是点预测。我们提出了一种方法，该方法生成的预测集（几乎完全）为训练总体周围的 f-散度球内的任何测试分布提供正确的覆盖水平。该方法基于共形推断，仅在训练数据可交换的条件下，就能在有限样本中实现（近乎）有效的覆盖。我们方法的一个关键组成部分是估计预期未来数据偏移的量，并对其构建鲁棒性；我们开发了估计器，并证明了它们在偏移下对不确定性估计的保护性和有效性的一致性。通过在多个大规模基准数据集上进行实验，包括 Recht 等人的 CIFAR-v4 和 ImageNet-V2 数据集，我们提供了互补的实证结果，突出了鲁棒预测有效性的重要性。
英文摘要：
While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.'s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
