# The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming

arXiv: https://arxiv.org/abs/2402.14844 · HTML: https://mlsanity.com/paper/2402.14844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles](https://mlsanity.com/paper/2002.04926/) (2020)
- [A Better Match for Drivers and Riders: Reinforcement Learning at Lyft](https://mlsanity.com/paper/2310.13810/) (2023)
- [Pearl: A Production-ready Reinforcement Learning Agent](https://mlsanity.com/paper/2312.03814/) (2023)
- [A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblems](https://mlsanity.com/paper/2302.07947/) (2023)
- [Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling](https://mlsanity.com/paper/2106.03322/) (2021)
- [Incrementality Bidding and Attribution](https://mlsanity.com/paper/2208.12809/) (2022)

## 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.