Fast classification rates without standard margin assumptions

We consider the classical problem of learning rates for classes with finite Vapnik-Chervonenkis (VC) dimension. It is well known that fast learning rates up to O(d/n) are achievable by the empirical risk minimization (ERM) algorithm if low noise or margin assumptions are satisfied. These usually require the optimal Bayes classifier to be in the class, and it has been shown that when this is not the case, the fast rates cannot be achieved even in the noise free case. In this paper, we further investigate the question of the fast rates in the agnostic setting, when the Bayes classifier is not in the class and the noise in the labeling is allowed. First, we consider classification with a reject option, namely Chow's reject option model, and show that by slightly lowering the impact of hard instances, a learning rate of order O (d/n log n/d) is always achievable in the agnostic setting by a specific learning algorithm. Similar results were only known under special versions of margin assumptions. As an auxiliary result, we show that under the Bernstein assumption the performance of the proposed algorithm is never worse than the performance of ERM even if some of the labels are predicted at random. Based on those results, we derive the necessary and sufficient conditions for classification (without a reject option) with fast rates in the agnostic setting achievable by improper learners. This simultaneously extends the work by Massart & Nedelec (2006, Ann. Stat., 34, 2326-2366), which studied this question in the case where the Bayesian optimal rule belongs to the class, and the work by Ben-David and Urner (2014, COLT, pp. 527-542), which allows the misspecification but is limited to the no noise setting. Our result also provides the first general setup in statistical learning theory in which an improper learning algorithm may significantly improve the learning rate for non-convex losses.