Real-life data are often non-IID due to complex distributions and interactions, and the sensitivity to the distribution of samples can differ among learning models. Accordingly, a key question for any supervised or unsupervised model is whether the probability distributions of two given datasets can be considered identical. To address this question, we introduce R-divergence, designed to assess model-oriented distribution discrepancies. The core insight is that two distributions are likely identical if their optimal hypothesis yields the same expected risk for each distribution. To estimate the distribution discrepancy between two datasets, R-divergence learns a minimum hypothesis on the mixed data and then gauges the empirical risk difference between them. We evaluate the test power across various unsupervised and supervised tasks and find that R-divergence achieves state-of-the-art performance. To demonstrate the practicality of R-divergence, we employ R-divergence to train robust neural networks on samples with noisy labels.

通过介绍 R-散度来评估模型导向的分布差异，我们评估了R-散度在各种无监督和有监督任务中的测试能力，并发现其实现了最先进的性能。为了证明 R-散度的实用性，我们利用 R-散度在带有噪声标签的样本上训练了鲁棒性神经网络。

用于估计面向模型的分布差异的R-散度