Summary of Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models, by Xueping Gong and Jiheng Zhang
Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models
by Xueping Gong, Jiheng Zhang
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 algorithm for contextual dynamic pricing achieves improved regret bounds while minimizing assumptions about the problem. The algorithm discretizes the unknown noise distribution and combines upper confidence bounds with a layered data partitioning technique to regulate regret in each episode. This approach effectively controls the regret associated with pricing decisions, leading to minimax optimality. The algorithm’s regret upper bound matches the lower bound up to logarithmic terms, demonstrating its effectiveness. Additionally, the method extends beyond linear valuation models by considering general function spaces and simplifies the estimation process using offline regression oracles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to set prices based on what customers want. They created an algorithm that works well even when there’s some uncertainty about how much customers value certain products. The algorithm is good at controlling the mistakes it makes when setting prices, and it can work with different types of customer preferences. This approach could help companies make more money by charging the right price for their products. |
Keywords
* Artificial intelligence * Regression
