mlcube

15 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 […]

15 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, […]

15 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. […]

15 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 […]

15 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 […]

15 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 […]

15 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 […]

15 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, […]

15 Feb 21

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. […]

22 Dec 20

ML cube co-founders won the Best Paper Award at NeurIPS 2020

We are happy to announce that our co-founders Alberto Marchesi and Nicola Gatti (together with Andrea Celli and Gabriele Farina) won the prestigious Best Paper Award at NeurIPS 2020 with a work about no-regret learning dynamics in sequential decision-making. The Neural Information Processing Systems (NeurIPS) conference is the most important AI and Machine Learning meeting, […]