国产a片

荔园学者Colloquium第一百八十七期

讲座题目：Feature-Rich, Data-Private: A Sparse Learning Framework for the High-Dimensional Newsvendor

主讲人：常晋源 教授（西南财经大学)

讲座时间：2026年11月22日14：30-15:30

讲座地点：国产a片 粤海校区汇研楼601会议室

内容摘要：Firms leveraging high-dimensional data for the feature-based newsvendor problem face a dual challenge: the operational risk of learning ineffective policies from a vast feature space and the strategic risk of leaking sensitive data. Standard privacy mechanisms are ill suited for this context, because the noise required for privacy often scales with the ambient dimension, overwhelming the sparse signals that drive demand. To resolve this tension, we develop a differentially private algorithm that synergistically combines a noisy variant of iterative hard thresholding with convolution-based smoothing of the newsvendor cost function. By carefully calibrating noise within the hard-thresholding step, our method simultaneously enforces model sparsity and provides rigorous privacy guarantees, whereas the smoothed surrogate stabilizes the iterative optimization process. We establish nonasymptotic error bounds that characterize the tradeoffs among privacy, sparsity, feature dimension, and sample size. Crucially, our analysis reveals that the algorithm’s performance depends on the sparsity budget (s) rather than the ambient dimension (p). We prove that the excess operational cost (regret) converges at an accelerated rate of O(sn^{-1}log p), a significant improvement over the standard rate O(p^{1/2}n^{-1/2}) for nonsmooth problems. These theoretical insights provide a principled justification for extensive feature engineering, enabling firms to leverage complex data sources without incurring a dimensionality penalty. An empirical study using real-world retail data corroborates our theory, demonstrating that the proposed framework provides a robust solution for achieving both operational efficiency and data security in modern inventory management.

主讲人简介：常晋源，西南财经大学光华首席教授、中国科学院数学与系统科学研究院研究员，主要从事大规模复杂数据分析相关的研究，先后担任统计学、计量经济学和运筹优化国际学术期刊Statistica Sinica、Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics、Journal of the American Statistical Association和Operations Research的副主编，获得过国务院政府特殊津贴、霍英东教育基金会高等院校青年科学奖一等奖和青年教师奖一等奖、教育部高等学校科学研究优秀成果奖、四川省青年科技奖等奖励，获四川省基础研究领衔科学家专项资助。

欢迎师生参加！

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　　　　　　　　　　　　　　　　　　2026年11月9日