Portrait of Erick Delage

Erick Delage

Associate Academic Member
erick.delage@hec.ca
Full Professor, HEC Montréal, Department of Decision Sciences
Research Topics
Optimization
Reinforcement Learning
Website
Google Scholar
LinkedIn

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
Abhilash Chenreddy
PhD - HEC Montréal
Esther Derman
Postdoctorate - Université de Montréal
Principal supervisor :
Pierre-Luc Bacon
Website
Github
Google Scholar
Lidiia Shchichko
PhD - Concordia University
Xiaohui Tu
PhD - HEC Montréal
Xin Wang
PhD - HEC Montréal
Weikai Wang
PhD - HEC Montréal

Publications

Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures
Option Pricing
Saeed Marzban
Erick Delage
Jonathan Yu-Meng Li
2023-09-05
Quantitative Finance (published)
doi.org
A double-oracle, logic-based Benders decomposition approach to solve the K-adaptability problem
A. Ghahtarani
A. Saif
A. Ghasemi
Erick Delage
2023-07-01
Computers & Operations Research (published)
doi.org
arxiv.org
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana
Abhilash Reddy Chenreddy
Erick Delage
Alexandre Forel
Emma Frejinger
Thibaut Vidal
2023-06-17
ArXiv (preprint)
doi.org
arxiv.org
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani
Erick Delage
Angelos Georghiou
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information t… (see more)o provide more anticipative decisions. The wide range of methods and contexts of application have motivated the design of a universal unitless measure of performance known as the coefficient of prescriptiveness. This coefficient was designed to quantify both the quality of contextual decisions compared to a reference one and the prescriptive power of side information. To identify policies that maximize the former in a data-driven context, this paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk minimization objective. We present a bisection algorithm to solve this model, which relies on solving a series of linear programs when the distributional ambiguity set has an appropriate nested form and polyhedral structure. Studying a contextual shortest path problem, we evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift.
2023-06-09
ArXiv (preprint)
doi.org
arxiv.org
Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs
Mehran Poursoltani
Erick Delage
Angelos Georghiou
2023-01-30
Operational Research (published)
doi.org
On Dynamic Program Decompositions of Static Risk Measures
Jia Lin Hau
Erick Delage
Mohammad Ghavamzadeh
Marek Petrik
Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming d… (see more)ecompositions. Prior work has proposed to use a dynamic decomposition of risk measures that help to formulate dynamic programs on an augmented state space. This paper shows that several existing decompositions are inherently inexact, contradicting several claims in the literature. In particular, we give examples that show that popular decompositions for CVaR and EVaR risk measures are strict overestimates of the true risk values. However, an exact decomposition is possible for VaR, and we give a simple proof that illustrates the fundamental difference between VaR and CVaR dynamic programming properties.
2023-01-01
arXiv.org (preprint)
doi.org
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