Summary of Private Optimal Inventory Policy Learning For Feature-based Newsvendor with Unknown Demand, by Tuoyi Zhao et al.
Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand
by Tuoyi Zhao, Wen-xin Zhou, Lan Wang
First submitted to arxiv on: 23 Apr 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 proposes a novel approach to estimate a privacy-preserving optimal inventory policy within the f-differential privacy framework, which is an extension of the classical (ε, δ)-differential privacy. The authors develop a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation. This approach simultaneously addresses three main challenges: unknown demand distribution and nonsmooth loss function, provable privacy guarantees for individual-level data, and desirable statistical precision. The proposed method achieves finite-sample high-probability bounds for optimal policy parameter estimation and regret analysis. The authors also derive a faster excess population risk bound compared to that obtained from an indiscriminate application of existing results for general nonsmooth convex loss. This aligns with the bound for strongly convex and smooth loss function. Numerical experiments demonstrate that the proposed method can achieve desirable privacy protection with a marginal increase in cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem where data-driven inventory planning is important, but individual customer data needs to be kept private. The authors create an algorithm to balance these competing goals. They develop a new way to estimate an optimal inventory policy that ensures each customer’s data remains private while still giving good results. This approach helps solve three big challenges: unknown demand distribution, nonsmooth loss function, and provable privacy guarantees. The paper shows how the proposed method can be used in practice by providing numerical examples. The results demonstrate that the algorithm can achieve good privacy protection with only a small increase in cost. |
Keywords
» Artificial intelligence » Gradient descent » Loss function » Precision » Probability
