It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain

However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this purpose.

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs.

We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization.