Time series analysis is ubiquitous and important in various areas, such as Artificial
Intelligence for IT Operations (AIOps) in cloud computing, AI-powered Business Intelligence …

Artificial intelligence (AI) has accelerated the advancement of financial services by
identifying hidden patterns from data to improve the quality of financial decisions. However …

Machine learning methods for fraud detection have achieved impressive prediction
performance, but often sacrifice critical interpretability in many applications. In this work, we …

Recent advances in Artificial Intelligence (AI) especially in deep learning have manifested
an increasing concern in trustworthiness, and its subparts such as interpretability, safety …

Minimizing the difference of two submodular (DS) functions is a problem that naturally
occurs in various machine learning problems. Although it is well known that a DS problem …

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 …

One of the potential solutions for model interpretation is to train a surrogate model: a more
transparent model that approximates the behavior of the model to be explained. Typically …

Interpretable models are crucial in many high-stakes decision-making applications. In this
paper, we focus on learning a decision rule list for binary and multi-class classification …

Rule set learning has long been studied and has recently been frequently revisited due to
the need for interpretable models. Still, existing methods have several shortcomings: 1) most …

In causal inference, estimating heterogeneous treatment effects (HTE) is critical for
identifying how different subgroups respond to interventions, with broad applications in …