no code implementations • 3 Aug 2023 • Benjamin Heymann, Alexandre Gilotte, Rémi Chan-Renous
We consider a repeated auction where the buyer's utility for an item depends on the time that elapsed since his last purchase.
1 code implementation • 5 Oct 2022 • Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
Aggregating a dataset, then injecting some noise, is a simple and common way to release differentially private data. However, aggregated data -- even without noise -- is not an appropriate input for machine learning classifiers. In this work, we show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
no code implementations • 8 Aug 2022 • Otmane Sakhi, David Rohde, Alexandre Gilotte
Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context.
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.
no code implementations • 18 Sep 2019 • Olivier Jeunen, Dmytro Mykhaylov, David Rohde, Flavian vasile, Alexandre Gilotte, Martin Bompaire
In order to handle this "bandit-feedback" setting, several Counterfactual Risk Minimisation (CRM) methods have been proposed in recent years, that attempt to estimate the performance of different policies on historical data.
no code implementations • 17 Sep 2019 • Alexandre Gilotte
Ranking metrics are a family of metrics largely used to evaluate recommender systems.
no code implementations • 22 Jan 2018 • Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec, Alexandre Abraham, Simon Dollé
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data.