Section: Bilateral Contracts and Grants with Industry

Bilateral Contracts with Industry


  • contract with http://Lelivrescolaire.fr; PI: Michal Valko

    Title: Sequential Machine Learning for Adaptive Educational Systems

    Duration: Mar. 2018 – Feb. 2021

    Abstract: Adaptive educational content are technologies which adapt to the difficulties encountered by students. With the rise of digital content in schools, the mass of data coming from education enables but also ask for machine learning methods. Since 2010, Lelivrescolaire.fr has been developing some learning materials for teachers and students through collaborative creation process. For instance, during the school year 2015/2016, students has achieved more than 8 000 000 exercises on its homework platform Afterclasse.fr. Our approach would be based on sequential machine learning: the algorithm learns to recommend some exercises which adapt to students gradually as they answer.

    Participants : Julien Seznec, Alessandro Lazaric, Michal Valko.


  • contract with “OtherLang”; PI: Romaric Gaudel

    Title: Tool to support foreign language practice

    Duration: 2 months

    Abstract: OtherLang develops an application to learn a foreign language by reading documents and interacting wit other people. During the time-line of the contract, SequeL brought his knowledge about Recommender Systems which may be used either to recommend documents to users or to recommend users to users.

    Participants : Romaric Gaudel, Philippe Preux.


  • contract with “Sidexa”; PI: Jérémie Mary and then Philippe Preux

    Title: vision applied to the segmentation and recognition of car body parts parts

    Duration: 3 months

    Abstract: We investigate deep learning to perform car body segmentation. The result being very good, a second contract will follow up this one in 2018.

    Participants : Jérémie Mary, Philippe Preux.


  • contract with “Renault”; PI: Philippe Preux

    Title: State of the art in reinforcement learning regarding autonomous car control and path planning.

    Duration: 3 months (Jan–Mar 2017)

    Abstract: This work has consisted in surveying the litterature related to autonomous car control, and reinforcement learning.

    Participants : Alexis Martin, Odalric Maillard, Philippe Preux.

  • contract with Renault; PI: Philippe Preux

    Title: Control of an autonomous vehicle

    Duration: 3 years (12/2017–11/2020)

    Abstract: This contract comes along the CIFRE grant on the same topic. This work is done in collaboration with the NON-A team-project.

    Participants : Édouard Leurent, Odalric Maillard, Philippe Preux.


  • contract with “Criteo”; PI: Philippe Preux

    Title: Computational advertizing

    Duration: 3 years (12/2017–11/2020)

    Abstract: This contract comes along the CIFRE grant on the same topic. The goal is to investigate reinforcmeent learning and deep learning on the problem of ad selection on the Internet.

    Participants : Philippe Preux, Kiewan Villatel.

Orange Labs

  • contract with “Orange Labs”; PI: Philippe Preux

    Title: Sequential Learning and Decision Making under Partial Monitoring

    Duration: Oct. 2014 – Sep. 2017

    Abstract: This contract comes along the CIFRE grant on the same topic. In applications such as recommendation systems, or computational advertising, the return collected from the user is partial: (s)he clicks on one item, or no item at all. We study this setting in which only a “partial” information is gathered in particular how to learn to behave optimaly in such a setting.

    Participants : Pratik Gajane, Philippe Preux.

Orange Labs

  • contract with “Orange Labs”; PI: Olivier Pietquin

    Title: Inter User Transfer in dialogue systems

    Duration: 3 years

    Abstract: This contract comes along the CIFRE grant on the same topic. The research aims at developing new algorithms to learn fast adaptation strategies for dialogue systems when a new user starts using them while we collected data from previous interactions with other users. Especially, it addresses the cold-start problem encountered when a new user faces the system, before samples can be collected to optimize the interaction strategy.

    Participants : Merwan Barlier, Nicolas Carrara, Olivier Pietquin.


  • contract with “55”; PI: Jérémie Mary

    Title: Novel Learning and Exploration-Exploitation Methods for Effective Recommender Systems

    Duration: Oct. 2015 – Sep. 2018

    Abstract: This contract comes along the CIFRE grant on the same topic. In this Ph.D. thesis we intend to deal with this problem by developing novel and more sophisticated recommendation strategies in which the collection of data and the improvement of the performance are considered as a unique process, where the trade-off between the quality of the data and the performance of the recommendation strategy is optimized over time. This work also consider tensor methods (one layer of the tensor can be the time) with the goal to scale them at RS level.