Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

19 May 2020

Manish Raghavan

Aleksandrs Slivkins

Jennifer Wortman Vaughan

Zhiwei Steven Wu

Papers citing "Greedy Algorithm almost Dominates in Smoothed Contextual Bandits"

9 / 9 papers shown

Title
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure Aleksandrs Slivkins Yunzong Xu Shiliang Zuo 86 1 0 06 Mar 2025
Exploration and Persuasion Aleksandrs Slivkins 34 12 0 22 Oct 2024
Incentivized Exploration via Filtered Posterior Sampling Anand Kalvit Aleksandrs Slivkins Yonatan Gur 24 1 0 20 Feb 2024
Repeated Bilateral Trade Against a Smoothed Adversary Nicolò Cesa-Bianchi Tommaso Cesari Roberto Colomboni Federico Fusco S. Leonardi 36 16 0 21 Feb 2023
Bandit Social Learning: Exploration under Myopic Behavior Kiarash Banihashem Mohammadtaghi Hajiaghayi Suho Shin Aleksandrs Slivkins 21 4 0 15 Feb 2023
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts Hongju Park Mohamad Kazem Shirani Faradonbeh 23 6 0 02 Feb 2022
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits Yash J. Patel Mohamad Kazem Shirani Faradonbeh 13 15 0 23 Oct 2021
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification James A. Grant David S. Leslie 44 3 0 29 Sep 2021
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment Eli Ben-Michael D. J. Greiner Kosuke Imai Zhichao Jiang OffRL 33 22 0 22 Sep 2021