14 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: recent empirical evidence favors richer models, while economic int…
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…
Conditional Risk Minimization with Side Information: A Tractable, Universal Optimal Transport Framework
Xinqiao Xie, Jonathan Yu-Meng Li
Conditional risk minimization arises in high-stakes decisions where risk must be assessed in light of side information, such as stressed economic conditions, specific customer prof…
Reconciling Risk-Aversion Paradoxes in the Distribution-Free Newsvendor Problem: Scarf's Rule Meets Dual Utility
Jonathan Yu-Meng Li, Tiantian Mao, Reza Valimoradi
How should a risk-averse newsvendor order optimally under distributional ambiguity? Attempts to extend Scarf's celebrated distribution-free ordering rule using risk measures have l…
Wasserstein-Kelly Portfolios: A Robust Data-Driven Solution to Optimize Portfolio Growth
Jonathan Yu-Meng Li
We introduce a robust variant of the Kelly portfolio optimization model, called the Wasserstein-Kelly portfolio optimization. Our model, taking a Wasserstein distributionally robus…
A General Wasserstein Framework for Data-driven Distributionally Robust Optimization: Tractability and Applications
Jonathan Yu-Meng Li, Tiantian Mao
Data-driven distributionally robust optimization is a recently emerging paradigm aimed at finding a solution that is driven by sample data but is protected against sampling errors.…
WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management
Saeed Marzban, Erick Delage, Jonathan Yumeng Li +2
The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be made over time with th…
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures
Saeed Marzban, Erick Delage, Jonathan Yumeng Li
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations either employ a static ri…
Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures
Saeed Marzban, Erick Delage, Jonathan Yumeng Li
In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the contract to the same level…
Inverse Optimization of Convex Risk Functions
Jonathan Yu-Meng Li
The theory of convex risk functions has now been well established as the basis for identifying the families of risk functions that should be used in risk averse optimization proble…