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Strategic Linear Contextual Bandits
Auteur(s)
Thomas Kleine Buening
Alan Turing Institute
Aadirupa Saha
Haifeng Xu
Date de parution
2024
In
The Thirty-eighth Annual Conference on Neural Information Processing Systems
De la page
1
A la page
38
Résumé
Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linear contextual bandit problem, where the arms can strategically misreport privately observed contexts to the learner. We treat the algorithm design problem as one of mechanism design under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that incentivizes the agents (i.e., arms) to report their contexts truthfully while simultaneously minimizing regret. We also show that failing to account for the strategic nature of the agents results in linear regret. However, a trade-off between mechanism design and regret minimization appears to be unavoidable. More broadly, this work aims to provide insight into the intersection of online learning and mechanism design.
Nom de l'événement
NeurIPS 2024
Lieu
Vancouver, Canada
Identifiants
Type de publication
conference output
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