# NEURIPS 2020

## Inverse Reinforcement Learning from a Gradient-based Learner

Inverse Reinforcement Learning from a Gradient-based Learner Authors: Giorgia Ramponi, Gianluca Drappo, Marcello Restelli Conference: NeurIPS 2020 Abstract: Inverse Reinforcement Learning addresses the problem of inferring an expert’s reward function from demonstrations. However, in many applications, we not only have access to the expert’s near-optimal behaviour, but we also observe part of her learning process. […]

## An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Authors: Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric Conference: NeurIPS 2020 Abstract: In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. In this paper, we […]

## 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 […]

## 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 […]

## Inverse Reinforcement Learning from a Gradient-based Learner

Inverse Reinforcement Learning from a Gradient-based Learner Authors: Giorgia Ramponi, Gianluca Drappo, Marcello Restelli Conference: NeurIPS 2020 Abstract: Inverse Reinforcement Learning addresses the problem of inferring an expert’s reward function from demonstrations. However, in many applications, we not only have access to the expert’s near-optimal behaviour, but we also observe part of her learning process. […]

## An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Authors: Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric Conference: NeurIPS 2020 Abstract: In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. In this paper, we […]

## 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 […]

## 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 […]