Section: Partnerships and Cooperations

International Initiatives


  • Title: Computational intelligence and Decision making

  • International Partner (Institution - Laboratory - Researcher):

    • NUTN (Taiwan) - Multimedia Informatics Lab - Chang-Shing Lee

  • Start year: 2015

  • See also: http://www.lri.fr/~teytaud/indema.html

  • The associate team works on computation intelligence for decision making, with different application fields for the various partners: - power systems (Tao) - eLearning (Oase) - games (Ailab)


  • Title: Threefold Scalability in Any-objective Black-Box Optimization

  • International Partner (Institution - Laboratory - Researcher):

    • Shinshu (Japan) - Tanaka-Hernan-Akimoto Laboratory - Hernan Aguirre

  • Start year: 2015

  • See also: http://francejapan.gforge.inria.fr/doku.php?id=associateteam

  • This associate team brings together researchers from the TAO and Dolphin Inria teams with researchers from Shinshu university in Japan. Additionally, researchers from the University of Calais are external collaborators to the team. The common interest is on black-box single and multi-objective optimization with complementary expertises ranging from theoretical and fundamental aspects over algorithm design to solving industrial applications. The work that we want to pursue in the context of the associate team is focused on black-box optimization of problems with a large number of decision variables and one or several functions to evaluate solutions, employing distributed and parallel computing resources. The objective is to theoretically derive, analyze, design, and develop scalable black-box stochastic algorithms including evolutionary algorithms for large-scale optimization considering three different axes of scalability: (i) decision space, (ii) objective space, and (iii) availability of distributed and parallel computing resources.

    We foresee that the associate team will make easier the collaboration already existing through a proposal funded by Japan and open-up a long term fruitful collaboration between Inria and Shinshu university. The collaboration will be through exchanging researchers and Ph.D. students and co-organization of workshops.

Informal International Partners

  • Marc Schoenauer partner of the ARC-DP (Australian Research Council Discovery Project) bio-inspired computing methods for dynamically changing environments. Coordinator: University of Adelaide (Frank Neumann), 5 years from Nov. 2015, 400 k$-AUS. Visit to Adelaide planned in Feb. 2017.

  • Isabelle Guyon partner of UC Berkeley Fingerprint verification with deep siamese neural networks using ultratonic sensor data. Co-advisor of a master student (Baiyu Chen). Partners: Alyosha Efros, Bernhard Boser.

Participation in Other International Programs

Indo-French Center of Applied Mathematics
  • Contextual multi-armed bandits with hidden structure

  • Title: Contextual multi-armed bandits with hidden structure

  • International Partner (Institution - Laboratory - Researcher):

    • IISc Bangalore (India) - ECE - Aditya Gopalan

  • Duration: 12 months - April 2017

  • Start year: April 2016

  • Recent advances in Multi-Armed Bandit (MAB) theory have yielded key insights into, and driven the design of applications in, sequential decision making in stochastic dynamical systems. Notable among these are recommender systems, which have benefited greatly from the study of contextual MABs incorporating user-specific information (the context) into the decision problem from a rigorous theoretical standpoint. In the proposed initiative, the key features of (a) sequential interaction between a learner and the users, and (b) a relatively small number of interactions per user with the system, motivate the goal of efficiently exploiting the underlying collective structure of users. The state-of-the-art lacks a wellgrounded strategy with provably near-optimal guarantees for general, low-rank user structure. Combining expertise in the foundations of MAB theory together with recent advances in spectral methods and low-rank matrix completion, we target the first provably near-optimal sequential low-rank MAB