Skip to yearly menu bar Skip to main content


Poster

Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms

Elvis Dohmatob · Meyer Scetbon


Abstract:

In this paper, we investigate the impact of test-time adversarial attacks on linear regression models and determine the optimal level of robustness that any model can reach while maintaining a given level of standard predictive performance (accuracy). Through quantitative estimates, we uncover fundamental tradeoffs between adversarial robustness and accuracy in different regimes. We obtain a precise characterization which distinguishes between regimes where robustness is achievable without hurting standard accuracy and regimes where a tradeoff might be unavoidable. Our findings are empirically confirmed with simple experiments that represent a variety of settings. This work covers feature covariance matrices and attack norms of any nature, extending previous works in this area.

Live content is unavailable. Log in and register to view live content