6 papers · 1 filter
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…
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…
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.…
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…