# Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets

arXiv: https://arxiv.org/abs/2409.03505 · HTML: https://mlsanity.com/paper/2409.03505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Generalizing Complex Hypotheses on Product Distributions: Auctions, Prophet Inequalities, and Pandora's Problem](https://mlsanity.com/paper/1911.11936/) (2019)
- [Data-Pooling in Stochastic Optimization](https://mlsanity.com/paper/1906.00255/) (2019)
- [Sample Complexity of Posted Pricing for a Single Item](https://mlsanity.com/paper/2406.00819/) (2024)
- [How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time](https://mlsanity.com/paper/2107.02742/) (2021)
- [Beyond IID: data-driven decision-making in heterogeneous environments](https://mlsanity.com/paper/2206.09642/) (2022)
- [VC Theory for Inventory Policies](https://mlsanity.com/paper/2404.11509/) (2024)
- [Dynamic Resource Allocation: Algorithmic Design Principles and Spectrum of Achievable Performances](https://mlsanity.com/paper/2205.09078/) (2022)
- [From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms](https://mlsanity.com/paper/2302.08424/) (2023)
- [Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective](https://mlsanity.com/paper/2407.04900/) (2024)
- [Dynamic Pricing for Reusable Resources: The Power of Two Prices](https://mlsanity.com/paper/2308.13822/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach](https://mlsanity.com/paper/2609.39397/) (2026)

## Abstract (arXiv)

In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is unknown, and one must work with samples from the distribution. Data-driven Newsvendor has been studied under many variants: additive vs. multiplicative regret, high probability vs. expectation bounds, and different distribution classes. This paper studies all combinations of these variants, filling in many gaps in the literature and simplifying many proofs. In particular, we provide a unified analysis based on the notion of clustered distributions, which in conjunction with our new lower bounds, shows that the entire spectrum of regrets between 1/√n and 1/n can be possible. Simulations on commonly-used distributions demonstrate that our notion is the "correct" predictor of empirical regret across varying data sizes.