The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming
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Abstract (arXiv)
In this paper, we explore a novel combination of supervised learning and quadratic programming to refine dynamic pricing models in the car rental industry. We utilize dynamic modeling of price elasticity, informed by ordinary least squares (OLS) metrics such as p-values, homoscedasticity, error normality. These metrics, when their underlying assumptions hold, are integral in guiding a quadratic programming agent. The program is tasked with optimizing margin for a given finite set target.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 19 Feb 2024.
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References · 7
- 2021Towards Causal Representation Learningcited by 207
- 2020Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oraclescited by 195
- 2023A Better Match for Drivers and Riders: Reinforcement Learning at Lyftcited by 9
- 2023Pearl: A Production-ready Reinforcement Learning Agentcited by 9
- 2023A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblemscited by 8
- 2021Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modelingcited by 3
- 2022Incrementality Bidding and Attributioncited by 3
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