8 papers
Generative Distributionally Robust Optimization
Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao Jin
Generative models are increasingly adopted in distributionally robust optimization (DRO), but existing approaches trade off model compatibility and adversarial structure: methods t…
Harnessing Heterogeneous Data for Conditional Optimization via Optimal Transport
Jonathan Yu-Meng Li, Qinyu Wu
Conditional optimization tailors decisions to contextual or event information, but its practical use is often limited by the difficulty of learning the relevant conditional distrib…
Sampler-Robust Optimization under Generative Models
Ziwei Zhang, Jonathan Yu-Meng Li
Modern stochastic optimization pipelines increasingly rely on learned generative models to represent uncertainty, while downstream decisions are evaluated almost entirely through M…
The Virtue of Sparsity in Complexity
Nima Afsharhajari, Jonathan Yu-Meng Li
Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: richer feature spaces appear to favor complexity, while economic i…
Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios
Saeed Asadi, Jonathan Yu-Meng Li
We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a…
Learning From Design Procedure To Generate CAD Programs for Data Augmentation
Yan-Ying Chen, Dule Shu, Matthew Hong +3
Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. O…