Our selection is possiblefor settings. InconcurrentChatterjiet(2019) studied wherethefirstK constantd-dimensional class.

Nonstationarity is prevalent in sequential decision making, which poses a critical challenge to the vast majority of existing approaches developed offline.

Our analysis highlights sufficient properties for a regret minimization algorithm to be used as leader.

V ov95, CBL06] and online convex optimization [Haz16, Ora19] have been developed. Until the labels of all examples of X have been predicted: The learning algorithm picks a point x X and makes a prediction z R about its label.

In this paper, we study the same problem in the Massart noise model that we now define.

Contextual bandit is a widely used model for sequential decision making.