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We study repeated first-price auctions and general repeated Bayesian games between two players, where one player, the learner, employs a no-regret learning algorithm, and the other player, the optimizer, knowing the learner's algorithm, strategizes to ...

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 equilibrium by giving (nonnegative) payments to players. We call this the ...

We study dynamic mechanisms for optimizing revenue in repeated auctions, that are robust to heterogeneous forward-looking and learning behavior of the buyers. Typically it is assumed that the buyers are either all myopic or are all infinite lookahead, ...

Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-...

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution D in every round). Prior work assumes that the buyer is fully rational and will ...

A plethora of recent work has analyzed properties of outcomes in games when each player employs a no-regret learning algorithm. Many algorithms achieve regret against the best fixed action in hindisght that decays at a rate of O(1/'T), when the game is ...

This paper investigates repeated security games with unknown (to the defender) game payoffs and attacker behaviors. As existing work assumes prior knowledge about either the game payoffs or the attacker's behaviors, they are not suitable for tackling ...

The traditional econometrics approach for inferring properties of strategic interactions that are not fully observable in the data, heavily relies on the assumption that the observed strategic behavior has settled at an equilibrium. This assumption is ...

The main goal of this paper is to develop a theory of inference of player valuations from observed data in the generalized second price auction without relying on the Nash equilibrium assumption. Existing work in Economics on inferring agent values from ...

In a Stackelberg Security Game, a defender commits to a randomized deployment of security resources, and an attacker best-responds by attacking a target that maximizes his utility. While algorithms for computing an optimal strategy for the defender to ...

We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting dif- ferential privacy both for the individuals in the database and for the analysts. That is, our mechanism's answer to each ...

Sensor relocation is a critical issue because it affects the coverage quality and capability of a mobile sensor network. In this paper, the problem of sensor relocation is formulated as a repeated multi-player game. At each step of repeated interactions,...

In this paper, we consider a cross-layer design of dynamic spectrum access in distributive cognitive radio (CR) networks. We model the licensed channel as a finite-state Markov channel (FSMC) and the CR user selects one channel to access and decides ...

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish ...