Adversarial Resilience in Sequential Prediction via Abstention Anonymous Author(s) Affiliation Address email

We study the problem of sequential prediction in the stochastic setting with an1 adversary that is allowed to inject clean-label adversarial (or out-of-distribution)2 examples. Algorithms designed to handle purely stochastic data tend to fail in the3 presence of such adversarial examples, often leading to erroneous predictions. This4 is undesirable in many high-stakes applications such as medical recommendations,5 where abstaining from predictions on adversarial examples is preferable to mis-6 classification. On the other hand, assuming fully adversarial data leads to very7 pessimistic bounds that are often vacuous in practice.8 To capture this motivation, we propose a new model of sequential prediction that9 sits between the purely stochastic and fully adversarial settings by allowing the10 learner to abstain from making a prediction at no cost on adversarial examples.11