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 …

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 …

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 …

We study the problem of characterizing optimal learning algorithms for playing repeated
games against an adversary with unknown payoffs. In this problem, the first player (called …

Learning to bid in repeated first-price auctions is a fundamental problem at the interface of
game theory and machine learning, which has seen a recent surge in interest due to the …

We consider the problem of steering no-regret-learning agents to play desirable equilibria
via nonnegative payments. We show that steering is impossible if the total budget (across …

Learning algorithms are often used to make decisions in sequential decision-making
environments. In multi-agent settings, the decisions of each agent can affect the …

A mediator observes no-regret learners playing an extensive-form game repeatedly across $
T $ rounds. The mediator attempts to steer players toward some desirable predetermined …

When deployed in the world, a learning agent such as a recommender system or a chatbot
often repeatedly interacts with another learning agent (such as a user) over time. In many …

When learning in strategic environments, a key question is whether agents can overcome
uncertainty about their preferences to achieve outcomes they could have achieved absent …