Improved Generalization Bounds for Robust Learning

Idan Attias, Aryeh Kontorovich, Yishay Mansour

Research output: Contribution to journalConference articlepeer-review

Abstract

We consider a model of robust learning in an adversarial environment. The learner gets uncorrupted training data with access to possible corruptions that may be effected by the adversary during testing. The learner’s goal is to build a robust classifier that would be tested on future adversarial examples. We use a zero-sum game between the learner and the adversary as our game theoretic framework. The adversary is limited to k possible corruptions for each input. Our model is closely related to the adversarial examples model of Schmidt et al. (2018); Madry et al. (2017). Our main results consist of generalization bounds for the binary and multi-class classification, as well as the real-valued case (regression). For the binary classification setting, we both tighten the generalization bound of Feige, Mansour, and Schapire (2015), and also are able to handle an infinite hypothesis class H. The sample complexity is improved from O( 14 log(|H|δ )) to O( 12 (k log(k) VC(H) + log 1δ )). Additionally, we extend the algorithm and generalization bound from the binary to the multiclass and real-valued cases. Along the way, we obtain results on fat-shattering dimension and Rademacher complexity of k-fold maxima over function classes; these may be of independent interest. For binary classification, the algorithm of Feige et al. (2015) uses a regret minimization algorithm and an ERM oracle as a blackbox; we adapt it for the multi-class and regression settings. The algorithm provides us with near optimal policies for the players on a given training sample.

Original languageEnglish
Pages (from-to)162-183
Number of pages22
JournalProceedings of Machine Learning Research
Volume98
StatePublished - 2019
Event30th International Conference on Algorithmic Learning Theory, ALT 2019 - Chicago, United States
Duration: 22 Mar 201924 Mar 2019

Keywords

  • Adversarial Learning
  • Fat-Shattering Dimension
  • Generalization Bounds
  • Rademacher Complexity
  • Robust Learning
  • Zero-Sum Game

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