Section: Partnerships and Cooperations

International Initiatives

Inria Associate Teams not involved in an Inria International Labs


In the end of 2015 SequeL started an Inria Associate team with CWI, Amsterdam. This project is called “Universal algorithms for sequential forecasting and bandit problems” and is led by Daniil Ryabko from the SequeL side, and by Peter Grunwald from the CWI side.

  • Title: Educational Bandits

  • International Partner (Institution - Laboratory - Researcher):

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

  • Inria investigators: A. Lazaric, M. Valko

  • 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

Declared Inria International Partners
Montanuniverstat Leoben

Montanuniverstat Leoben (MUL), Austria, is an international partner of SequeL . The work in 2015 has been mostly on representation learning in reinforcement learning. The partnership involves Ronald Ortner and Peter Auer on the MUL side.

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.

  • Politecnico di Milano (Italy)

    • Nicola Gatti Collaborator

    • A. Lazaric finalized a work with N. Gatti on the application of MAB on sponsored search auctions and mechanism design.

  • Universität Potsdam (Germany)

    • Alexandra Carpentier Collaborator

    • M. Valko collaborates with A. Carpentier on scaling bandits to large dimensions and structures.

  • Adobe Research, California

    • Branislav Kveton Collaborator

      M. Valko and B. Kveton collaboration for sequential learning at recommendation for the entertainment content that features diversity.

  • Boston University, USA

    • Venkatesh Saligrama Collaborator

      M. Valko, R. Munos collaborated with V. Saligrama and M. Hanawal, on cost-effective spectral sensing, useful in radars.