arXiv：cs.LG· Wenbin Zhou, Elizabeth Cucuzzella, Shixiang Zhu·· 3 小时前
诊断传输 DRO：用留出校准数据自适应调整歧义集几何
Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
AI 导读
DT-DRO 通过留出校准数据，利用条件概率积分变换 CDF 诊断系统性概率错配，并以结果级传输同时调整歧义集中心与 ground cost，使歧义集几何匹配观测到的预测误差。该形式化可给出计算可行的对偶重构，并推导出随估计与近似误差消失而收紧的歧义半径和决策风险保证，可消除模型误设导致的非消失鲁棒性地板。合成实验与停电应用显示，在结构和尾部误设下决策质量提升。
正文
Abstract:Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and yield overly conservative decisions. We introduce diagnostic-transport DRO (DT-DRO), which uses held-out calibration data to adapt the ambiguity-set geometry to observed predictive errors. DT-DRO uses the conditional probability integral transform cumulative distribution function to diagnose systematic probability misallocation and translates this information into an outcome-level transport that jointly adjusts the ambiguity-set center and ground cost. The resulting formulation admits a computationally tractable dual reformulation. Theoretically, we derive valid ambiguity radii and decision-risk guarantees that tighten as estimation and approximation errors vanish, and show that DT-DRO can eliminate the nonvanishing robustness floor caused by model misspecification. Synthetic experiments and a power-outage application demonstrate improved decision quality, particularly under structural and tail misspecification.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.10793 [stat.ML] |
| (or arXiv:2610.10793v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10793 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenbin Zhou [view email]
[v1]
Wed, 7 Oct 2026 18:51:43 UTC (1,895 KB)
来源：arXiv：cs.LG · arxiv.org