On Randomized Algorithms in Online Strategic Classification

TL;DR

This paper explores the advantages of randomized algorithms in online strategic classification, providing new lower bounds and improved algorithms that outperform deterministic methods in both realizable and agnostic settings.

Contribution

It extends lower bounds to randomized algorithms, introduces a new randomized learner with improved bounds, and develops an optimal proper learner with tighter regret guarantees.

Findings

First lower bound for randomized learners in realizable setting.

New randomized learner improves existing upper bounds.

Proper learner achieves near-optimal regret bounds.

Abstract

Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions. For example, given a classifier that determines loan approval based on credit scores, applicants may open or close credit cards and bank accounts to obtain a positive prediction. The learning goal is to achieve low mistake or regret bounds despite such strategic behavior. While randomized algorithms have the potential to offer advantages to the learner in strategic settings, they have been largely underexplored. In the realizable setting, no lower bound is known for randomized algorithms, and existing lower bound constructions for deterministic learners can be circumvented by randomization. In the agnostic setting, the best known regret upper bound is $O(T^{3/4}\log^{1/4}T|\mathcal H|)$, which is far from the standard online learning rate of $O(\sqrt{T\log|\mathcal H|})$. In this work, we provide refined bounds for online strategic classification in both settings. In the realizable setting, we extend, for $T > \mathrm{Ldim}(\mathcal{H}) \Delta^2$, the existing lower bound $\Omega(\mathrm{Ldim}(\mathcal{H}) \Delta)$ for deterministic learners to all learners. This yields the first lower bound that applies to randomized learners. We also provide the first randomized learner that improves the known (deterministic) upper bound of $O(\mathrm{Ldim}(\mathcal H) \cdot \Delta \log \Delta)$. In the agnostic setting, we give a proper learner using convex optimization techniques to improve the regret upper bound to $O(\sqrt{T \log |\mathcal{H}|} + |\mathcal{H}| \log(T|\mathcal{H}|))$. We show a matching lower bound up to logarithmic factors for all proper learning rules, demonstrating the optimality of our learner among proper learners. As such, improper learning is necessary to further improve regret guarantees.