Section: Partnerships and Cooperations

International Initiatives

Inria Associate Teams Not Involved in an Inria International Labs

  • Title: Educational Bandits

  • International Partner (Institution - Laboratory - Researcher):

    • Carnegie Mellon University (United States) - Department of Computer Science, Theory of computation lab - Emma Brunskill

  • Start year: 2015

  • See also: https://project.inria.fr/eduband/

  • Education can transform an individual's capacity and the opportunities available to him. The proposed collaboration will build on and develop novel machine learning approaches towards enhancing (human) learning. Massive open online classes (MOOCs) are enabling many more people to access education, but mostly operate using status quo teaching methods. Even more important than access is the opportunity for online software to radically improve the efficiency, engagement and effectiveness of education. Existing intelligent tutoring systems (ITSs) have had some promising successes, but mostly rely on learning sciences research to construct hand-built strategies for automated teaching. Online systems make it possible to actively collect substantial amount of data about how people learn, and offer a huge opportunity to substantially accelerate progress in improving education. An essential aspect of teaching is providing the right learning experience for the student, but it is often unknown a priori exactly how this should be achieved. This challenge can often be cast as an instance of decision-making under uncertainty. In particular, prior work by Brunskill and colleagues demonstrated that reinforcement learning (RL) and multi-arm bandit (MAB) can be very effective approaches to solve the problem of automated teaching. The proposed collaboration is thus intended to explore the potential interactions of the fields of online education and RL and MAB. On the one hand, we will define novel RL and MAB settings and problems in online education. On the other hand, we will investigate how solutions developed in RL and MAB could be integrated in ITS and MOOCs and improve their effectiveness.

Inria International Partners

With CWI
  • Title: Learning theory

  • “North-European Associate Team”

    • Centrum Wiskunde & Informatica (CWI), Amsterdam (NL) - Peter Grünwald

  • Duration: 2016 - 2018

  • Start year: 2016

  • ABSTRACT: The aim is to develop the theory of learning for sequential decision making under uncertainty problems.

    In 2016, this collaboration involved D. Ryabko, É. Kaufmann, J. Ridgway, M. Valko, A. Lazaric, O. Maillard. A post-doc funded by Inria has been recruited in Fall 2016.

    This collaboration aims at developing through the Inria International Laboratory with CWI.

With University of Leoben
  • Title: The multi-armed bandit problem

  • International Partner (Institution - Laboratory - Researcher):

    • University of Leoben (Austria) - Peter Auer

  • Duration: 2016 - 2016

  • Start year: 2016

  • ABSTRACT: Study of the multi-armed bandit problem.

Informal International Partners
  • University of California Irvine (USA)

    • Anima Anandkumar Collaborator

    • A. Lazaric collaborates with A. Anandkumar on the use of spectral methods for reinforcement learning.

  • University of Lancaster (UK)

    • Borja Balle Collaborator

    • O-A. Maillard collaborates with B. Balle on concentration inequalities for Hankel matrices.