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Section: Bilateral Contracts and Grants with Industry
Bilateral Contracts with Industry
Lelivrescolaire.fr
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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, Michal Valko.
Sidexa
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contract with “Sidexa”; PI: Philippe Preux
Title: vision applied to the segmentation and recognition of cars and car related documents.
Abstract: this is a follow-up to the successful contract realized in 2017 with Sidexa. We studied multi-class supervised classification problems in order to classify documents related to a car, and also to identify various characteristics of a car, such as its color, its make, its type.
This work is done with an InriaTech engineer.
Participant : Philippe Preux.
Renault
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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.
Critéo
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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 reinforcement learning and deep learning on the problem of ad selection on the Internet.
Participants : Philippe Preux, Kiewan Villatel.
Orange Labs
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contract with “Orange Labs”; PI: Olivier Pietquin
Title: Inter User Transfer in dialogue systems
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.
55
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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.
The PhD was defended in Fall 2018.
Participants : Jérémie Mary, Romain Warlop.
AB-Tasty
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Thompson Sampling for A/B/C Testing with Delayed Conversions; PI: Émilie Kaufmann
Abstract: We investigated the use of Thompson Sampling as well as other state-of-the-art methods for the stochastic MAB problem in the context of delayed feedback. We provided theoretical justification for a method developed by AB Tasty, and proposed some variants of it, as well as a comparison with existing methods from the literature.
Participant : Émilie Kaufmann.