no code implementations • 12 Jun 2024 • Mohammadtaghi Hajiaghayi, Sébastien Lahaie, Keivan Rezaei, Suho Shin
In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity.
no code implementations • 13 Feb 2024 • Simina Brânzei, Mohammadtaghi Hajiaghayi, Reed Phillips, Suho Shin, Kun Wang
Alice cuts the cake at a point of her choice, while Bob chooses the left piece or the right piece, leaving the remainder for Alice.
no code implementations • 28 Dec 2023 • Seyed Esmaeili, Mohammadtaghi Hajiaghayi, Suho Shin
We consider Bayesian agents who are unaware of ex-post realization of their own arms' mean rewards, which is the first to study Bayesian extension of Shin et al. (2022).
no code implementations • 13 Dec 2023 • Seyed A. Esmaeili, Suho Shin, Aleksandrs Slivkins
We identify a class of MAB algorithms which we call performance incentivizing which satisfy a collection of properties and show that they lead to mechanisms that incentivize top level performance at equilibrium and are robust under any strategy profile.
no code implementations • 11 Nov 2023 • Soheil Feizi, Mohammadtaghi Hajiaghayi, Keivan Rezaei, Suho Shin
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems.
no code implementations • 7 Oct 2023 • Mohammadtaghi Hajiaghayi, Mohammad Mahdavi, Keivan Rezaei, Suho Shin
To mitigate this behavior, the principal announces an eligible set which screens out a certain set of solutions.
no code implementations • 15 Feb 2023 • Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins
We study social learning dynamics motivated by reviews on online platforms.
no code implementations • 23 Oct 2021 • Suho Shin, Seungjoon Lee, Jungseul Ok
We consider a multi-armed bandit problem in which a set of arms is registered by each agent, and the agent receives reward when its arm is selected.