Macro Mondrian Conformal Risk Control：分组风险控制新方法
Macro Mondrian Conformal Risk Control
做分割或不确定度估计的可以看看，CRC 在稀有类别上会失控，这篇用分组重加权来解决，还给了两个数据集的实验。
论文提出 Macro Mondrian Conformal Risk Control 方法，针对 Conformal Risk Control（CRC）只提供边际风险控制的缺陷。在数据分布不均衡时，即使边际保证成立，含稀有目标的输入风险仍可能失控。新方法基于 Mondrian conformal prediction 和 macro-coverage 算法，通过 taxonomy 函数给每个数据点分配分组，并按组概率对风险重加权，使稀有样本权重更高。作者在两个语义分割数据集上演示了该方法。
Macro Mondrian Conformal Risk Control
Conformal Risk Control (CRC) extends conformal prediction beyond the coverage setting to the control of general monotone loss functions, in a distribution-free and modelagnostic fashion. This makes it well-suited to problems with high-dimensional targets, such as the semantic segmentation task addressed in this work. However, CRC guarantees only marginal control, meaning that the risk is controlled on average over the data distribution. Under imbalanced distributions, the risk on inputs that contain rare objects may therefore remain uncontrolled, even when the marginal guarantee holds. We propose a new method that controls the risk on average over user-defined groups of the domain, drawing on Mondrian conformal prediction and macro-coverage algorithms. The method relies on a taxonomy, that is, a function that assigns a group to each data point, and reweights each point's risk by a coefficient that depends on its group probability, so that rare points contribute more than common ones. We illustrate the approach on two semantic segmentation datasets.