kmlcube

18 Feb 21

No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium

No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium Authors: Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti Conference: NeurIPS 2020 Abstract: The existence of simple, uncoupled no-regret dynamics that converge to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems. Specifically, it has been known for more than 20 years […]
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18 Feb 21

Online Bayesian Persuasion

Online Bayesian Persuasion Authors: Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti Conference: NeurIPS 2020 Abstract: In Bayesian persuasion, an informed sender has to design a signaling scheme that discloses the right amount of information so as to influence the behavior of a self-interested receiver. This kind of strategic interaction is ubiquitous in real economic […]
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18 Feb 21

Sequential transfer in reinforcement learning with a generative model

Sequential transfer in reinforcement learning with a generative model Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli Conference: ICML 2020 Abstract: We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. The availability of solutions to related problems poses a […]
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18 Feb 21

Control frequency adaptation via action persistence in batch reinforcement learning

Control frequency adaptation via action persistence in batch reinforcement learning Authors: Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli Conference: ICML 2020 Abstract: The choice of the control frequency of a system has a relevant impact on the ability of reinforcement learning algorithms to learn a highly performing policy. In this paper, […]
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18 Feb 21

Driving exploration by maximum distribution in gaussian process bandits

Driving exploration by maximum distribution in gaussian process bandits Authors: Alessandro Nuara, Francesco Trovò, Dominic Crippa, Nicola Gatti, Marcello Restelli Conference: AAMAS 2020 Abstract: The problem of finding optimal solutions of stochastic functions over continuous domains is common in several real-world applications, such as, e.g., advertisement allocation, dynamic pricing, and power control in wireless networks. […]
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18 Feb 21

Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces

Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces Authors: Alberto Marchesi, Francesco Trovò, Nicola Gatti Conference: AAMAS 2020 Abstract: We tackle the problem of learning equilibria in simulation-based games. In such games, the players’ utility functions cannot be described analytically, as they are given through a black-box simulator that can […]
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18 Feb 21

A combinatorial-bandit algorithm for the online joint bid/budget optimization of pay-per-click advertising campaigns

A combinatorial-bandit algorithm for the online joint bid/budget optimization of pay-per-click advertising campaigns Authors: Alessandro Nuara, Francesco Trovo, Nicola Gatti, Marcello Restelli Conference: AAAI 2018 Abstract: Pay-per-click advertising includes various formats (eg, search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some […]
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18 Feb 21

Dealing with interdependencies and uncertainty in multi-channel advertising campaigns optimization

Dealing with interdependencies and uncertainty in multi-channel advertising campaigns optimization Authors: Alessandro Nuara, Nicola Sosio, Francesco TrovÃ, Maria Chiara Zaccardi, Nicola Gatti, Marcello Restelli Conference: WWW 2019 Abstract: In 2017, Internet ad spending reached 209 billion USD worldwide, while, e.g., TV ads brought in 178 billion USD. An Internet advertising campaign includes up to thousands […]
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18 Feb 21

Targeting optimization for internet advertising by learning from logged bandit feedback

Targeting optimization for internet advertising by learning from logged bandit feedback Authors: Margherita Gasparini, Alessandro Nuara, Francesco Trovò, Nicola Gatti, Marcello Restelli Conference: IJCNN 2018 Abstract: In the last two decades, online advertising has become the most effective way to sponsor a product or an event. The success of this advertising format is mainly due […]
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18 Feb 21

A characterization of quasi-perfect equilibria

A characterization of quasi-perfect equilibria Authors: Nicola Gatti, Mario Gilli, Alberto Marchesi Journal: Games and Economic Behavior Abstract: We provide a characterization of quasi-perfect equilibria in n-player games, showing that any quasi-perfect equilibrium can be obtained as limit point of a sequence of Nash equilibria of a certain class of perturbed games in sequence form, […]
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