Summary of Learning to Price Homogeneous Data, by Keran Chen et al.
Learning to Price Homogeneous Data
by Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 paper studies a data pricing problem where a seller has access to homogeneous data points and multiple types of buyers with different valuation curves. The seller aims to learn the revenue-optimal pricing curve through online learning, given limited information about the distribution of buyers. To solve this problem, the authors develop novel discretization schemes to approximate any pricing curve, which scales gracefully with the approximation parameter. They then extend classical algorithms like UCB and FTPL to account for asymmetric feedback and deal with the vast space of pricing curves. The paper achieves regret bounds of (m) in the stochastic setting and (m^{3/2}) in the adversarial setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to price data when you have many different types of buyers. The sellers want to learn the best way to price their data so they can make as much money as possible. They use a special kind of learning called online learning, where they can repeat the market many times to figure out what works best. To do this, they developed new ways to break down the pricing curves into smaller pieces, which helps them learn faster and make better decisions. The paper shows that their approach works well in both situations where the buyers are randomly distributed and when they try to trick the sellers. |
Keywords
* Artificial intelligence * Online learning
