论文标题
具有统计保证的稳健基于流的共形推理（FCI）
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee
论文作者
论文摘要
共形预测旨在确定使用过去的经验对新对象的预测的精确信心。但是，培训数据和测试数据之间常用的可交换假设限制了其在处理受污染的测试集时的使用。在本文中，我们开发了一种新型的基于流动的共形推理（FCI）方法来构建对复杂和高维数据的预测集和推断异常值。我们利用从对抗流的想法将输入数据传输到具有已知分布的随机向量。我们的往返转换可以将输入数据映射到一个低维空间，同时保留给定每个类标签的输入数据的条件分布，这使我们能够为不确定性量化构建不合格得分。当测试数据被污染时，我们的方法适用且健壮。我们在基准数据集上评估了我们的方法，基于流量的共形推理。我们发现它会产生有效的预测集和准确的异常检测，并且相对于竞争方法更有力量。
Conformal prediction aims to determine precise levels of confidence in predictions for new objects using past experience. However, the commonly used exchangeable assumptions between the training data and testing data limit its usage in dealing with contaminated testing sets. In this paper, we develop a novel flow-based conformal inference (FCI) method to build predictive sets and infer outliers for complex and high-dimensional data. We leverage ideas from adversarial flow to transfer the input data to a random vector with known distributions. Our roundtrip transformation can map the input data to a low-dimensional space, meanwhile reserving the conditional distribution of input data given each class label, which enables us to construct a non-conformity score for uncertainty quantification. Our approach is applicable and robust when the testing data is contaminated. We evaluate our method, robust flow-based conformal inference, on benchmark datasets. We find that it produces effective predictive sets and accurate outlier detection and is more powerful relative to competing approaches.
