# Learning Strategy-Aware Linear Classifiers

arXiv: https://arxiv.org/abs/1911.04004 · HTML: https://mlsanity.com/paper/1911.04004/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [Impact of Decentralized Learning on Player Utilities in Stackelberg Games](https://mlsanity.com/paper/2403.00188/) (2024)
- [The Role of Causal Features in Strategic Classification for Robustness and Alignment](https://mlsanity.com/paper/2605.27163/) (2026)

## Abstract (arXiv)

We address the question of repeatedly learning linear classifiers against agents who are strategically trying to game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms. First, we show that Stackelberg and external regret for the problem of strategic classification are strongly incompatible: i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa. Second, we present a strategy-aware algorithm for minimizing the Stackelberg regret for which we prove nearly matching upper and lower regret bounds. Finally, we provide simulations to complement our theoretical analysis. Our results advance the growing literature of learning from revealed preferences, which has so far focused on "smoother" assumptions from the perspective of the learner and the agents respectively.