Data-driven Conditional Robust Optimization
Speakers
Erick Delage
Professor, HEC Montréal
Erick Delage is a professor in the Department of Decision Sciences at HEC Montréal, a chairholder of the Canada Research Chair in decision making under uncertainty, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. His research interests span the areas of robust and stochastic optimization, decision analysis, reinforcement learning, and risk management with applications to portfolio optimization, inventory management, energy, and transportation problems.
| Date: |
| Wednesday, 7 May 2025 |
| Time: |
| 10:00 am - 11:30 am |
| Venue: |
|
NUS Business School Mochtar Riady Building BIZ1-0302 15 Kent Ridge Drive Singapore 119245 (Map) |
Abstract
Conditional Robust Optimization (CRO) is a decision-making framework that blends the flexibility of robust optimization (RO) with the ability to incorporate additional information regarding the structure of uncertainty. This approach solves the RO problem where the uncertainty set structure adapts to account for the most recent information provided by a set of covariates. In this presentation, we will introduce two data-driven approaches to CRO: a sequential predict-then-optimize method and an integrated end-to-end method. We will also show how hypothesis testing can be integrated to the training in order to improve the quality of conditional coverage of the produced uncertainty sets.