Robustly Reliable Learning for Regression

We introduce the problem of robustly reliable learning for the regression setting in adversarial environments. Given a test point $x_{test}$, a training dataset $S$ of $n$ data points, a corruption budget $b \ll n$, and a real-valued hypothesis class $\mathcal H$, our goal is to output the optimal p