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We study a variant of decision-theoretic online learning in which the set ofexperts that are available to Learner can shrink over time. This is arestricted version of the well-studied sleeping experts problem, itself ageneralization of the fundamental game of prediction with expert advice.Similar to many works in this direction, our benchmark is the ranking regret.Various results suggest that achieving optimal regret in the fully adversarialsleeping experts problem is computationally hard. This motivates our relaxationwhere any expert that goes to sleep will never again wake up. We call thissetting "dying experts" and study it in two different cases: the case where thelearner knows the order in which the experts will die and the case where thelearner does not. In both cases, we provide matching upper and lower bounds onthe ranking regret in the fully adversarial setting. Furthermore, we presentnew, computationally efficient algorithms that obtain our optimal upper bounds.

Dying Experts: Efficient Algorithms with Optimal Regret Bounds