As such, in games where the marginalization of CCE coincides with the game's Nash equilibria (like

Consider a recommendation platform that interacts with a finite set of users in an online fashion. Users arrive at the platform and receive a recommendation.

The latter case can be further reduced to adversarial MDP when preferences only depend on the final state.

Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.

In this paper, we introduce a generalization of the standard Stackelberg Games (SGs) framework: Calibrated Stackelberg Games (CSGs) .