In this paper, we introduce a generalization of the standard Stackelberg Games (SGs)
framework: Calibrated Stackelberg Games. In CSGs, a principal repeatedly interacts with an …

We provide a novel reduction from swap-regret minimization to external-regret minimization,
which improves upon the classical reductions of Blum-Mansour and Stoltz-Lugosi in that it …

No-regret learners seek to minimize the difference between the loss they cumulated through
the actions they played, and the loss they would have cumulated in hindsight had they …

We consider the problem of evaluating forecasts of binary events whose predictions are
consumed by rational agents who take an action in response to a prediction, but whose …

We give a simple and computationally efficient algorithm that, for any constant ε> 0, obtains ε
T-swap regret within only T=(n) rounds; this is an exponential improvement compared to the …

We consider a number of questions related to tradeoffs between reward and regret in
repeated gameplay between two agents. To facilitate this, we introduce a notion of …

Many real-life contractual relations differ completely from the clean, static model at the heart
of principal-agent theory. Typically, they involve repeated strategic interactions of the …

A central problem in the theory of multi-agent reinforcement learning (MARL) is to
understand what structural conditions and algorithmic principles lead to sample-efficient …

We consider the problem of repeatedly auctioning a single item to multiple iid buyers who
each use a no-regret learning algorithm to bid over time. In particular, we study the seller's …

We study a repeated Principal Agent problem between a long lived Principal and Agent pair
in a prior free setting. In our setting, the sequence of realized states of nature may be …