no code implementations • 11 Apr 2024 • Mohammed Lahsaini, Mohamed Lechiakh, Alexandre Maurer
Recommendation algorithms (RS) used by social media, like YouTube, significantly shape our information consumption across various domains, especially in healthcare.
no code implementations • 17 Jul 2021 • Mohamed Lechiakh, Alexandre Maurer
The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function.
no code implementations • 29 May 2021 • Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo, Sergei Volodin, Dalia Papuc, Orfeas Liossatos, Ben Crulis, Mariame Tighanimine, Isabela Constantin, Anastasiia Kucherenko, Alexandre Maurer, Felix Grimberg, Vlad Nitu, Chris Vossen, Sébastien Rouault, El-Mahdi El-Mhamdi
We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project.
no code implementations • 7 Jun 2018 • El Mahdi El Mhamdi, Rachid Guerraoui, Lê Nguyên Hoang, Alexandre Maurer
We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones where each population is a majority.
no code implementations • 29 May 2018 • Henrik Aslund, El Mahdi El Mhamdi, Rachid Guerraoui, Alexandre Maurer
We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adversary.
1 code implementation • 21 Feb 2018 • El Mahdi El Mhamdi, Rachid Guerraoui, Alexandre Maurer, Vladislav Tempez
A standard belief on emerging collective behavior is that it emerges from simple individual rules.
no code implementations • NeurIPS 2017 • El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners.
Multi-agent Reinforcement Learning reinforcement-learning +1