Probing Gap between Learning and Evaluation Criteria

Abstract

While machine learning has achieved dramatic successes recently, a numerous researches have risen concerns against its deficiency such as lack of robustness and fairness. This tendency can be observed even for the cutting edge model architectures and training algorithms. Why does this unexpectancy happen? In this talk, we focus on the discrepancy between objective functions that learning algor thms optimize and evaluation criteria that ultimately define the goodness of learned models. By clearly distinguishing them, it enables us to verify whether the learning algorithms do achieve our desired properties and design suitable learning criteria. Specifically, I will introduce our recent work on adversarial robust classification and similarity learning.