Summary of Localized Exploration in Contextual Dynamic Pricing Achieves Dimension-free Regret, by Jinhang Chai et al.
Localized exploration in contextual dynamic pricing achieves dimension-free regret
by Jinhang Chai, Yaqi Duan, Jianqing Fan, Kaizheng Wang
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LetC algorithm is designed to solve the problem of contextual dynamic pricing with a linear demand model. By starting with a pure exploration stage, followed by refinement and finally exploitation, the algorithm achieves a minimax optimal, dimension-free regret bound when the time horizon exceeds a polynomial of the covariate dimension. A general theoretical framework is also provided, demonstrating how to balance exploration and exploitation when the horizon is limited. The analysis is powered by a novel critical inequality that depicts the exploration-exploitation trade-off in dynamic pricing. Experiments on synthetic and real-world data validate the theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re studying how to set prices based on context, like time of day or weather. To do this, we created an algorithm called LetC. It starts by exploring different prices, then refines its search and finally settles on the best price. This algorithm does a great job of finding the right balance between trying new things and sticking with what works. We tested it on fake and real data and saw that it worked well in both cases. |
