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 prediction range $I^*_{x_{test}}(S, b)$, such that any $L$-Lipschitz predictor consistent with all but at most $b$ points in $S$ must predict a value $h(x_{test}) \in I$. We then instantiate the analysis for $L$-Lipschitz hypothesis classes, a natural and widely studied class in the regression setting, focusing on two key scenarios. First, for the restricted case where the corrupted dataset is g...
| Authors | Avrim Blum, Anh Tuan Nguyen |
|---|---|
| Primary category | cs.LG |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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