The ROC curve is the major tool for assessing not only the performance but
also the fairness properties of a similarity scoring function in Face
Recognition. In order to draw reliable conclusions based on empirical ROC
analysis, evaluating accurately the uncertainty related to statistical versions
of the ROC curves of interest is necessary. For this purpose, we explain in
this paper that, because the True/False Acceptance Rates are of the form of
U-statistics in the case of similarity scoring, the naive bootstrap approach is
not valid here and that a dedicated recentering technique must be used instead.
This is illustrated on real data of face images, when applied to several
ROC-based metrics such as popular fairness metrics.

本研究介绍了针对人脸识别中 ROC 曲线的统计版本与偏差问题，基于经验 ROC 分析，因相似度评分的 True/False Acceptance Rates 呈 U - 统计学形式，导致朴素的自助法方法不适用，需采用专门的重新校准技术，以此计算准确的 ROC 曲线。本研究采用数张人脸图像为实验数据，分析了多种基于 ROC 的公平性指标。

面部识别中性能和公平指标的评估 - Bootstrap 方法

Assessing Performance and Fairness Metrics in Face Recognition - Bootstrap Methods

Existing Markov Chain Monte Carlo (MCMC) methods are either based on
general-purpose and domain-agnostic schemes which can lead to slow convergence,
or hand-crafting of problem-specific proposals by an expert. We propose
A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
produce samples with desired properties. First, we propose an efficient
likelihood-free adversarial training method to train a Markov chain and mimic a
given data distribution. Then, we leverage flexible volume preserving flows to
obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
train efficient Markov chains to sample from a prescribed posterior
distribution by iteratively improving the quality of both the model and the
samples. A-NICE-MC provides the first framework to automatically design
efficient domain-specific MCMC proposals. Empirical results demonstrate that
A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
deep neural networks, and is able to significantly outperform competing methods
such as Hamiltonian Monte Carlo.

本研究提出了一种新方法 A-NICE-MC，它训练了灵活的参数化 Markov 链内核以产生具有所需特性的样本，该方法利用了有效的无似然对抗训练方法来训练 Markov 链并模拟给定的数据分布，使用灵活的保体积流来获得 MCMC 的参数化内核。通过迭代地提高模型和样本的质量，使用自助法来训练有效的 Markov 链从规定的后验分布中进行采样。实证结果表明，A-NICE-MC 结合了 MCMC 的强大保证和深度神经网络的表现力，并能够显著优于竞争方法，如 Hamiltonian Monte Carlo。