Prediction with Corrupted Expert Advice

Prediction with expert advice is perhaps the single most fundamental problem in online learning and sequential decision making. In this problem, the goal of a learner is to aggregate decisions from multiple experts and achieve performance that approaches that of the best individual expert in hindsight. The standard performance criterion is the regret: the difference between the loss of the learner and that of the best single expert. The experts problem is often considered in the so-called adversarial setting, where the losses of the individual experts may be virtually arbitrary and even be chosen by an adversary so as to maximize the learner's regret. The canonical algorithm in this setup is the Multiplicative Weights algorithm (Littlestone and Warmuth, 1989; Freund and Schapire, 1995), that guarantees an optimal regret of Θ( T log N) in any problem with N experts and T decision rounds. A long line of research in online learning has focused on obtaining better regret guarantees, often referred to as "fast rates," on benign problem instances in which the loss generation process behaves more favourably than in a fully adversarial setup. A prototypical example of such an instance is the stochastic setting of the experts problem, where the losses of the experts are drawn i.i.d.