Adversarial Sign-Corrupted Isotonic Regression

Author name not available (Why is that?)

Publication date: 14 July 2022

Abstract: Classical univariate isotonic regression involves nonparametric estimation under a monotonicity constraint of the true signal. We consider a variation of this generating process, which we term adversarial sign-corrupted isotonic ( exttt{ASCI}) regression. Under this exttt{ASCI} setting, the adversary has full access to the true isotonic responses, and is free to sign-corrupt them. Estimating the true monotonic signal given these sign-corrupted responses is a highly challenging task. Notably, the sign-corruptions are designed to violate monotonicity, and possibly induce heavy dependence between the corrupted response terms. In this sense, exttt{ASCI} regression may be viewed as an adversarial stress test for isotonic regression. Our motivation is driven by understanding whether efficient robust estimation of the monotone signal is feasible under this adversarial setting. We develop exttt{ASCIFIT}, a three-step estimation procedure under the exttt{ASCI} setting. The exttt{ASCIFIT} procedure is conceptually simple, easy to implement with existing software, and consists of applying the exttt{PAVA} with crucial pre- and post-processing corrections. We formalize this procedure, and demonstrate its theoretical guarantees in the form of sharp high probability upper bounds and minimax lower bounds. We illustrate our findings with detailed simulations.

Has companion code repository: https://github.com/shamindras/ascifit

This page was built for publication: Adversarial Sign-Corrupted Isotonic Regression

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6405008)