1 code implementation • 23 Feb 2023 • Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
1 code implementation • 11 Feb 2022 • Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
no code implementations • 31 Jan 2022 • Eustache Diemert, Romain Fabre, Alexandre Gilotte, Fei Jia, Basile Leparmentier, Jérémie Mary, Zhonghua Qu, Ugo Tanielian, Hui Yang
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry.
1 code implementation • 19 Nov 2021 • Eustache Diemert, Artem Betlei, Christophe Renaudin, Massih-Reza Amini, Théophane Gregoir, Thibaud Rahier
Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the granular level.
no code implementations • 17 Dec 2020 • Artem Betlei, Eustache Diemert, Massih-Reza Amini
In real life scenarios, when we do not have access to ground-truth individual treatment effect, the capacity of models to do so is generally measured by the Area Under the Uplift Curve (AUUC), a metric that differs from the learning objectives of most of the Individual Treatment Effect (ITE) models.
no code implementations • 7 Aug 2020 • Thibaud Rahier, Amélie Héliou, Matthieu Martin, Christophe Renaudin, Eustache Diemert
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains.
1 code implementation • 22 Apr 2020 • Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
no code implementations • 20 Jul 2017 • Eustache Diemert, Julien Meynet, Pierre Galland, Damien Lefortier
Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry.