no code implementations • 7 Mar 2024 • Abhilash Chenreddy, Erick Delage
The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty.
no code implementations • 17 Jun 2023 • Utsav Sadana, Abhilash Chenreddy, Erick Delage, Alexandre Forel, Emma Frejinger, Thibaut Vidal
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
no code implementations • 9 Jun 2023 • 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 to provide more anticipative decisions.
no code implementations • NeurIPS 2023 • Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik
However, we show that these popular decompositions for Conditional-Value-at-Risk (CVaR) and Entropic-Value-at-Risk (EVaR) are inherently suboptimal regardless of the discretization level.
1 code implementation • 28 Oct 2022 • Rui Fan, Erick Delage
This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget.
no code implementations • 29 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Li
In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL) that can exploit more effectively cross-asset dependency information and achieve better performance than state-of-the-art architectures.
no code implementations • 29 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Li
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider coherent risk measures.
1 code implementation • 14 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Yumeng Li, Jeremie Desgagne-Bouchard, Carl Dussault
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 the consideration of many factors such as investors preferences, trading environments, and market conditions.
no code implementations • 9 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Yumeng Li
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures.
1 code implementation • 19 May 2021 • Abderrahim Fathan, Erick Delage
Optimal stopping is the problem of deciding the right time at which to take a particular action in a stochastic system, in order to maximize an expected reward.
1 code implementation • 30 Mar 2021 • Viet Anh Nguyen, Fan Zhang, Shanshan Wang, Jose Blanchet, Erick Delage, Yinyu Ye
Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.
no code implementations • NeurIPS 2020 • Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye
Conditional estimation given specific covariate values (i. e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences.