Mese: Febbraio 2021

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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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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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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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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Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving

Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving Authors: Nicola Gatti, Mario Gilli, Alberto MarchesiAmarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Riccardo Giol, Marcello Restelli, Danilo Romano Journal: Robotics and Autonomous Systems Abstract: The design of high-level decision-making systems is a topical problem in the field of autonomous driving. […]
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