Section: Bilateral Contracts and Grants with Industry
Bilateral Contracts with Industry
contract with http://Lelivrescolaire.fr; PI: Michal Valko
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.
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.
Participants : Jérémie Mary, Philippe Preux.
Participants : Alexis Martin, Odalric Maillard, Philippe Preux.
Participants : Édouard Leurent, Odalric Maillard, Philippe Preux.
Participants : Philippe Preux, Kiewan Villatel.
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.
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.
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.