  We present and study models of adversarial online learning where the feedback
observed by the learner is noisy, and the feedback is either full information
feedback or bandit feedback. Specifically, we consider binary losses xored with
the noise, which is a Bernoulli random variable. We consider both a constant
noise rate and a variable noise rate. Our main results are tight regret bounds
for learning with noise in the adversarial online learning model.
