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On Selfish Behavior in CSMA/CA Networks

Publications associées (46)

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Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Efstratios Panteleimon Skoulakis

In this paper we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU), for convergence to \textit{Coarse Correlated Equilibria} (CCE) in general games. CMWU effectively corresponds to the standard MWU algorithm but where ...

2022

Bandit Online Learning of Nash Equilibria in Monotone Games

Maryam Kamgarpour

We address online bandit learning of Nash equilibria in multi-agent convex games. We propose an algorithm whereby each agent uses only obtained values of her cost function at each joint played action, lacking any information of the functional form of her c ...

2021

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually al ...

2021

In this paper, we introduce a model of a financial market as a multiagent repeated game where the players are market makers. We formalize the concept of market making and the parameters of the game. Our main contribution is a framework that combines game t ...

2020

Destination buffer analysis for packets rejection obliteration in multi-channel networks

Georgia Fragkouli

In this paper we study a multi-channel network, each station of which is equipped with a network interface that has a receiver buffer of multiple packets. In this way, each station is able to receive multiple packets per time instant. We adopt a synchronou ...

2019

To quantify a player's commitment in a given Nash equilibrium of a finite dynamic game, we map the corresponding normal-form game to a "canonical extension," which allows each player to adjust his or her move with a certain probability. The commitment meas ...

2019

No-Regret Learning in Unknown Games with Correlated Payoffs

Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic

We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable i ...

Curran Associates, Inc.2019

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We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player ...

2018

Robust Multidimensional Pricing: Separation without Regret

Daniel Kuhn, Napat Rujeerapaiboon, Cagil Kocyigit

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavors to sell multiple goods to a single buyer, only knowing that the buyer's values for the goods range over a rectangular uncertainty set. We interpret this pr ...

2018

On Risk Reduction in Kelly Betting Using the Conservative Expected Value

Daniel Kuhn, Napat Rujeerapaiboon

The celebrated Kelly betting strategy guarantees, with probability one, higher long-run wealth than any other causal investment strategy. However, on the way to its long-term supremacy, this strategy has a notable downfall: it typically displays high varia ...

2018