We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound.

In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games .

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 online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

In many applications, learning systems are required to process continuous non-stationary data streams.

The circles and crosses represent the object and goal positions, respectively.