论文标题
部分可观测时空混沌系统的无模型预测
Renyi Fair Information Bottleneck for Image Classification
论文作者
论文摘要
我们开发了一种新的方法来确保机器学习中的公平性，我们将其称为Renyi公平信息瓶颈（RFIB）。对于学习公平表示形式，我们考虑了两种不同的公平约束 - 人口统计学奇偶校验和均衡的几率 - 并通过使用Renyi的Divergence与其可调参数$α$来得出损失函数，并考虑了效用，公平性和表达的紧凑性的三重约束。然后，我们使用Eyepacs Medical Imaging数据集评估了图像分类的方法的性能，表明它的表现优于竞争状态的最先进技术的性能，并使用各种复合效用/公平指标（包括准确性差距和Rawls的最低准确性）测量的性能。
We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair representations and derive a loss function via a variational approach that uses Renyi's divergence with its tunable parameter $α$ and that takes into account the triple constraints of utility, fairness, and compactness of representation. We then evaluate the performance of our method for image classification using the EyePACS medical imaging dataset, showing it outperforms competing state of the art techniques with performance measured using a variety of compound utility/fairness metrics, including accuracy gap and Rawls' minimal accuracy.
