Summary of Survey Of Data-driven Newsvendor: Unified Analysis and Spectrum Of Achievable Regrets, by Zhuoxin Chen et al.
Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets
by Zhuoxin Chen, Will Ma
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates various aspects of the data-driven Newsvendor problem, which aims to predict an unknown distribution’s mean. The study explores different variants of regret, probability bounds, and distribution classes, filling in gaps in the existing literature and simplifying proofs. A unified analysis is presented based on clustered distributions, revealing that the range of regrets between 1/√n and 1/n can be achievable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to guess a number drawn from an unknown distribution. This problem has many different versions depending on what kind of regret you want to minimize. The study makes progress in understanding all these different cases, making it easier to analyze some proofs. It also shows that the regrets can range from 1/√n to 1/n. |
Keywords
» Artificial intelligence » Probability
