Nonparametric Pricing Analytics with Customer Covariates
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Publication:5003728
DOI10.1287/opre.2020.2016zbMath1471.91161arXiv1805.01136OpenAlexW3132995834MaRDI QIDQ5003728
Guillermo Gallego, Ningyuan Chen
Publication date: 29 July 2021
Published in: Operations Research (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1805.01136
Microeconomic theory (price theory and economic markets) (91B24) Consumer behavior, demand theory (91B42)
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Policy Optimization Using Semiparametric Models for Dynamic Pricing ⋮ A Primal–Dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint
Cites Work
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- The multi-armed bandit problem with covariates
- Woodroofe's one-armed bandit problem revisited
- Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates
- Optimal aggregation of classifiers in statistical learning.
- Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems
- Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies
- Dynamic Pricing for Nonperishable Products with Demand Learning
- Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms
- Dynamic Pricing with a Prior on Market Response
- Dynamic Pricing Under a General Parametric Choice Model
- Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation
- The Continuum-Armed Bandit Problem
- Blind Network Revenue Management
- Online Decision Making with High-Dimensional Covariates
- A linear response bandit problem
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
- Improved Rates for the Stochastic Continuum-Armed Bandit Problem
- Prediction, Learning, and Games
- The elements of statistical learning. Data mining, inference, and prediction
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