However, achieving such an assumption is non-trivial, leading to the following two aspects of challenges. On the one hand, it is challenging to design a model with enough capacity with limited computational resources, andexisting models areusually tailored forspecific problems which require heavytrailand-error [25,57,59].

Experimental results showthattheproposed metriclearning algorithm improves both certified robust errors and empirical robust errors (errors under adversarial attacks).

In this paper, we focus on testing for safety.

Quantifying uncertainty is important for actionable predictions in real-world applications.