Distribution shifts in trustworthy machine learning

Abstract

This article investigates the impact of distribution shifts in trustworthy machine learning. To this end, we start by summarizing fine-grained types of distribution shifts commonly studied in machine learning communities. To tackle distribution shifts across domains, we present our research across various learning scenarios by enforcing knowledge transferability and trustworthiness. Specifically, we focus on two learning paradigms to improve knowledge transferability: distribution-informed representation learning and distribution-guided information propagation. Besides, we also explore how trustworthiness properties of a learning algorithm are affected by distribution shifts across domains. Finally, we discuss the open questions and future directions for handling distribution shifts in the era of large language models.