Keywords: [ Optimization ] [ Robustness ] [ Machine Learning ] [ Theory ] [ Adversarial Robustness and Security ]

Abstract:
Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the adversarial loss with most hypothesis sets is NP-hard. But, which surrogate losses should be used and when do they benefit from theoretical guarantees? We present an extensive study of this question, including a detailed analysis of the $\mathcal{H}$-calibration and $\mathcal{H}$-consistency of adversarial surrogate losses. We show that convex loss functions, or the supremum-based convex losses often used in applications, are not $\mathcal{H}$-calibrated for common hypothesis sets used in machine learning. We then give a characterization of $\mathcal{H}$-calibration and prove that some surrogate losses are indeed $\mathcal{H}$-calibrated for the adversarial zero-one loss, with common hypothesis sets. In particular, we fix some calibration results presented in prior work for a family of linear models and significantly generalize the results to the nonlinear hypothesis sets. Next, we show that $\mathcal{H}$-calibration is not sufficient to guarantee consistency and prove that, in the absence of any distributional assumption, no continuous surrogate loss is consistent in the adversarial setting. This, in particular, proves that a claim made in prior work is inaccurate. Next, we identify natural conditions under which some surrogate losses that we describe in detail are $\mathcal{H}$-consistent. We also report a series of empirical results which show that many $\mathcal{H}$-calibrated surrogate losses are indeed not $\mathcal{H}$-consistent, and validate our theoretical assumptions. Our adversarial $\mathcal{H}$-consistency results are novel, even for the case where $\mathcal{H}$ is the family of all measurable functions.

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