Trustworthy Machine Learning: Robustness, Generalization, and Interpretability

Abstract

Machine learning is becoming increasingly important in today's world. Beyond its powerful performances, there has been an emerging concern about the trustworthiness of machine learning, including but not limited to: robustness to malicious attacks, generalization to unseen datasets, and interpretability to explain its outputs. Such concerns are even more urgent in some safety-critical applications such as medical diagnosis and autonomous driving. Trustworthy machine learning (TrustML) aims to tackle these challenges from the perspectives of theory, algorithm, and applications. In this tutorial, we will give a comprehensive introduction to the recent advance of trustworthy machine learning in robustness, generalization, and interpretability. We will cover their problem formulation, related research, popular algorithms, and successful applications. Additionally, we will also introduce some potential challenges for future research. We do hope that this tutorial will not only serve as a platform to understand TrustML, but also raise the awareness of everyone for more trustworthy applications.

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