Author

Jonathan Yu-Meng Li

University of Ottawa

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Preprint Oct 2026

Distributions or Quantiles? A Convex Variational Theory of Worst-Case Risk

Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures. Using spectral risk measures as the underlying class, we develop a convex variational theory that unifies these analyses and extend...

Jian-Jun Gao, Jonathan Yu-Meng Li, Zhen Wang et al. · 0 citations

#machine learning Apr 2026

The Virtue of Sparsity in Complexity

Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: recent empirical evidence favors richer models, while economic intuition has long favored parsimony. We reconcile this tension by distinguishing capacity sparsity-restrictions on effective model capacity-fr...

Nima Afsharhajari, Jonathan Yu-Meng Li · arXiv.org · 0 citations

Jul 2026

Generative Distributionally Robust Optimization

This work proposes Generative Distributionally Robust Optimization (GDRO), a principled framework that accepts any sampleable conditional generator as the nominal model and restricts worst-case laws to a chosen conditional generator family.

Zi-Wei Zhang, Jonathan Yu-Meng Li, Zhihao Jin · arXiv.org · 0 citations

Preprint Jul 2026

Harnessing Heterogeneous Data for Conditional Optimization via Optimal Transport

A distributionally robust framework based on optimal transport (OT) for harnessing such heterogeneous data in conditional optimization and demonstrating the value of the framework through a conditional assortment problem using demand and product-feature data from multiple stores is demonstrated.

Jonathan Yu-Meng Li, Qin-Yu Wu · 0 citations