1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
FairJob: A Real-World Dataset for Fairness in Online Systems
Mariia Vladimirova, Federico Pavone, Eustache Diemert
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…
Sequential Counterfactual Risk Minimization
Houssam Zenati, Eustache Diemert, Matthieu Martin +2
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…
Efficient Kernel UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert +3
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…
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Eustache Diemert, Romain Fabre, Alexandre Gilotte +6
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed und…