Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Diemert, Eustache"'
We introduce a fairness-aware dataset for job recommendations in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business confidentiality
Externí odkaz:
http://arxiv.org/abs/2407.03059
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. In this paper, we explore the case where it is possible to deploy learned pol
Externí odkaz:
http://arxiv.org/abs/2302.12120
Autor:
Zenati, Houssam, Bietti, Alberto, Diemert, Eustache, Mairal, Julien, Martin, Matthieu, Gaillard, Pierre
In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. 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
Externí odkaz:
http://arxiv.org/abs/2202.05638
Autor:
Diemert, Eustache, Fabre, Romain, Gilotte, Alexandre, Jia, Fei, Leparmentier, Basile, Mary, Jérémie, Qu, Zhonghua, Tanielian, Ugo, Yang, Hui
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed under the Improving Web Advertising Business Group at W3C only allows sha
Externí odkaz:
http://arxiv.org/abs/2201.13123
Autor:
Diemert, Eustache, Betlei, Artem, Renaudin, Christophe, Amini, Massih-Reza, Gregoir, Théophane, Rahier, Thibaud
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. It represents a problem of growing interest in multiple se
Externí odkaz:
http://arxiv.org/abs/2111.10106
We consider the task of optimizing treatment assignment based on individual treatment effect prediction. This task is found in many applications such as personalized medicine or targeted advertising and has gained a surge of interest in recent years
Externí odkaz:
http://arxiv.org/abs/2012.09897
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health
Externí odkaz:
http://arxiv.org/abs/2008.03235
Autor:
Zenati, Houssam, Bietti, Alberto, Martin, Matthieu, Diemert, Eustache, Gaillard, Pierre, Mairal, Julien
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of learning stochastic policies with continuous actions from the viewpoint
Externí odkaz:
http://arxiv.org/abs/2004.11722
Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) th
Externí odkaz:
http://arxiv.org/abs/1707.06409
Autor:
Betlei, Artem, Gregoir, Théophane, Rahier, Thibaud, Bissuel, Aloïs, Diemert, Eustache, Amini, Massih-Reza
Individual Treatment Effect (ITE) estimation has become one of the main trends in Causal Inference due to its applications in various areas where personalization is key. In order to circumvent the complex problem of causal identification, the randomi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::df4879e2582beae28471985e0e302ea3
https://hal.archives-ouvertes.fr/hal-03339723/file/PPML_2021_ADUM_V1.pdf
https://hal.archives-ouvertes.fr/hal-03339723/file/PPML_2021_ADUM_V1.pdf