Prediction against a limited adversary
Erhan Bayraktar, Ibrahim Ekren, Xin Zhang; 22(72):1−33, 2021.
We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert. Using tools from viscosity theory, we characterize the long-time behavior of the value function of the game between the forecaster and the adversary. We provide lower and upper bounds for the growth rate of regret without relying on a comparison result. We show that depending on the description of regret, the limiting behavior of the game can significantly differ.
|© JMLR 2021. (edit, beta)|