WASSERSTEIN DISTRIBUTIONALLY ROBUST NONPARAMETRIC REGRESSION
来源：
发布时间：2026-06-03
报告时间：- 2026年6月15日（周一）上午10:30-11:30
- 报告地点：
- 国交2号楼315会议室
- 报告人：
- 刘常钰
- 报告人单位：
- 武汉大学武汉数学与智能研究院
- 邀请人：
- 黄佳雨
- 摘要：
- Wasserstein distributionally robust optimization (WDRO) strengthens statistical learning under model uncertainty by minimizing the local worstcase risk within a prescribed ambiguity set. Although WDRO has been extensively studied in parametric settings, its theoretical properties in nonparametric frameworks remain underexplored. This paper investigates WDRO for nonparametric regression. We first establish a structural distinction based on the order k of the Wasserstein distance, showing that k = 1 induces Lipschitz-type regularization, whereas k > 1 corresponds to gradient-norm regularization. To address model misspecification, we analyze the excess local worst-case risk, deriving non-asymptotic error bounds for estimators constructed using norm-constrained feedforward neural networks. This analysis is supported by new covering number and approximation bounds that simultaneously control both the function and its gradient. The proposed estimator achieves a convergence rate of n−2β/d+2β up to logarithmic factors, where β depends on the target’s smoothness and network parameters. This rate is shown to be minimax optimal under conditions commonly satisfied in high-dimensional settings. Moreover, these bounds on the excess local worstcase risk imply guarantees on the excess natural risk, ensuring robustness against any distribution within the ambiguity set. We show the framework’s generality across regression and classification problems. Simulation studies and an application to the MNIST dataset further illustrate the estimator’s robustness.
刘常钰（武汉大学武汉数学与智能研究院）
