Improving multi-armed bandit algorithms in online pricing settings

DOI10.1016/j.ijar.2018.04.006zbMath1452.91155OpenAlexW2801206439MaRDI QIDQ1644914

Nicola Gatti, Marcello Restelli, Stefano Paladino, Francesco Trovò

Publication date: 22 June 2018

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2018.04.006

zbMATH Keywords

pricingmulti-armed banditnonstationary MAB

Mathematics Subject Classification ID

Microeconomic theory (price theory and economic markets) (91B24) Online algorithms; streaming algorithms (68W27)

Related Items (1)

Setting Reserve Prices in Second-Price Auctions with Unobserved Bids

Cites Work

• Unnamed Item
• Unnamed Item
• Free trial or no free trial: optimal software product design with network effects
• Exploration-exploitation tradeoff using variance estimates in multi-armed bandits
• Asymptotically efficient adaptive allocation rules
• A note on an inequality involving the normal distribution
• Online learning in online auctions
• Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies
• On Upper-Confidence Bound Policies for Switching Bandit Problems
• Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms
• Dynamic Pricing Under a General Parametric Choice Model
• Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
• Concentration Inequalities and Martingale Inequalities: A Survey
• The Nonstochastic Multiarmed Bandit Problem
• Partial Monitoring—Classification, Regret Bounds, and Algorithms
• The sample complexity of revenue maximization
• Probability Inequalities for Sums of Bounded Random Variables
• Regret Minimization Under Partial Monitoring
• Prediction, Learning, and Games
• Finite-time analysis of the multiarmed bandit problem

This page was built for publication: Improving multi-armed bandit algorithms in online pricing settings