Higher-Order Certified Robustness for Regression
Randomized smoothing has emerged as a scalable technique for certifying the adversarial robustness of classifiers. However, its application to regression remains under-explored and faces unique challenges. Existing regression certificates rely on probabilistic acceptance regions and fail to exploit the local geometry of the function. In this work, we present a novel framework for certified robust regression that addresses these limitations. We derive a prediction-centered certificate that guarantees the stability of the smoothed model's prediction and ensures practical computability at test time. We investigate several alternatives for constructing these certificates by explicitly incorpo...
| Authors | Jie Zhang, Natalie Frank |
|---|---|
| Primary category | stat.ML |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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