Improved generalization bounds for robust learning.

consider a model of robust learning in an adversarial environment. The learner gets uncorrupted training data with access to possible corruptions that may be used by the adversary during testing. Their aim is to build a robust classifier that would be tested on future adversarially corrupted data. 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 one of Schmidt et al. (2018) and Madry et al. (2017). We refer to binary and multi-class classification settings, and regression setting. Our main results are generalization bounds for all settings. the binary classification setting, we improve a generalization bound previously found in Feige et al. (2015). generalize to the case of weighted average of hypotheses from $H$ that is not limited to be finite. The sample complexity has been improved from $O(frac{1}{epsilon^4}log(frac{|H|}{delta}))$ to $O(frac{1}{epsilon^2}(klog(k)VC(H)+logfrac{1}{delta}))$. The core of all is proofs based on bounds of the empirical Rademacher complexity. For the binary classification, we use a known regret minimization algorithm of Feige et al. that uses an ERM oracle as a blackbox and we expand on the multi-class and regression settings. The algorithm provides us near optimal policies for the players on a given training sample. The learner starts with a fixed hypothesis class $H$ and chooses a convex combination of hypotheses from $H$. The learneru0027s loss is measured on adversarial corrupted inputs. 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.