Portrait of Erick Delage

Erick Delage

Associate Academic Member
Full Professor, HEC Montréal, Department of Decision Sciences
Research Topics
Optimization
Reinforcement Learning
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Biography

Erick Delage is a professor in the Department of Decision Sciences at HEC Montréal, 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.

Delage’s research interests span the areas of robust and stochastic optimization, decision analysis, machine learning, reinforcement learning and risk management. He focuses on the applications of these processes to portfolio optimization, inventory management, and energy and transportation problems.

Current Students

Nima Akbarzadeh
Postdoctorate - HEC Montréal
Esther Derman
Collaborating Alumni - Université de Montréal
Principal supervisor :
Pierre-Luc Bacon
Website
Github
Google Scholar
Yudong Luo
Postdoctorate - HEC Montréal
Lidiia Shchichko
PhD - Concordia University
Xiaohui Tu
PhD - HEC Montréal
Weikai Wang
PhD - HEC Montréal

Publications

Data-Driven Optimization with Distributionally Robust Second Order Stochastic Dominance Constraints
Chun Peng
Erick Delage
This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic domina… (see more)nce constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy.
2022-11-22
Operational Research (published)
doi.org