Over the past decade, the field of neural networks has made significant strides, particularly
in deep learning. However, their limited interpretability has constrained their application in …

Though neural networks have achieved impressive prediction performance, it's still hard for
people to understand what neural networks have learned from the data. The black-box …

In this paper, we present a framework to deal with the fraud detection task with extremely few
labeled frauds. We involve human intelligence in the loop in a labor-saving manner and …

While neural networks have been achieving increasingly significant excitement in solving
classification tasks such as natural language processing, their lack of interpretability …

Rules are widely used in Fintech institutions to make fraud prevention decisions, since rules
are highly interpretable thanks to their intuitive if-then structure. In practice, a two-stage …

In this paper, we consider the problem of long tail scenario modeling with budget limitation,
ie, insufficient human resources for model training stage and limited time and computing …

Decision rules have been widely applied in industrial applications such as finance,
medicine, and biology, due to the critical requirement of interpretability. In order to make …

Identifying the fraud risk of applications on the web platform is a critical challenge with both
requirements of effectiveness and interpretability. In these high-stakes web applications …