Section: Partnerships and Cooperations

International Initiatives

Inria International Labs

International Laboratory for Research in Computer Science and Applied Mathematics

Associate Team involved in the International Lab:

SIMERG2E

• Title: Statistical Inference for the Management of Extreme Risks, Genetics and Global Epidemiology

• International Partner (Institution - Laboratory - Researcher):

  • UGB (Senegal) Abdou Kâ Diongue

• Start year: 2018

• See also: http://mistis.inrialpes.fr/simerge

• SIMERG2E is built on the same two research themes as SIMERGE, with some adaptations to new applications: 1) Spatial extremes, application to management of extreme risks. We address the definition of new risk measures, the study of their properties in case of extreme events and their estimation from data and covariate information. Our goal is to obtain estimators accounting for possible variability, both in terms of space and time, which is of prime importance in many hydrological, agricultural and energy contexts. 2) Classification, application to genetics and global epidemiology. We address the challenge to build statistical models in order to test association between diseases and human host genetics in a context of genome-wide screening. Adequate models should allow to handle complexity in genomic data (correlation between genetic markers, high dimensionality) and additional statistical issues present in data collected from a family-based longitudinal survey (non-independence between individuals due to familial relationship and non-independence within individuals due to repeated measurements on a same person over time).

Inria Associate Teams Not Involved in an Inria International Labs

LANDER

• Title: Latent Analysis, Adversarial Networks, and DimEnsionality Reduction

• International Partner (Institution - Laboratory - Researcher):

  • La Trobe university, Melbourne (Australia) - Department of Mathematics - Hien Nguyen

• Start year: 2019

• See also: https://team.inria.fr/mistis/projects/lander/

• The collaboration is based on three main points, in statistics, machine learning and applications: 1) clustering and classification (mixture models), 2) regression and dimensionality reduction (mixture of regression models and non parametric techniques) and 3) high impact applications (neuroimaging and MRI). Our overall goal is to collectively combine our resources and data in order to develop tools that are more ubiquitous and universal than we could have previously produced, each on our own. A wide class of problems from medical imaging can be formulated as inverse problems. Solving an inverse problem means recovering an object from indirect noisy observations. Inverse problems are therefore often compounded by the presence of errors (noise) in the data but also by other complexity sources such as the high dimensionality of the observations and objects to recover, their complex dependence structure and the issue of possibly missing data. Another challenge is to design numerical implementations that are computationally efficient. Among probabilistic models, generative models have appealing properties to meet all the above constraints. They have been studied in various forms and rather independently both in the statistical and machine learning literature with different depths and insights, from the well established probabilistic graphical models to the more recent (deep) generative adversarial networks (GAN). The advantages of the latter being primarily computational and their disadvantages being the lack of theoretical statements, in contrast to the former. The overall goal of the collaboration is to build connections between statistical and machine learning tools used to construct and estimate generative models with the resolution of real life inverse problems as a target. This induces in particular the need to help the models scale to high dimensional data while maintaining our ability to assess their correctness, typically the uncertainty associated to the provided solutions.

Inria International Partners

Informal International Partners

The context of our research is also the collaboration between mistis and a number of international partners such as the statistics department of University of Michigan, in Ann Arbor, USA, the statistics department of McGill University in Montreal, Canada, Université Gaston Berger in Senegal and Universities of Melbourne and Brisbane in Australia.

The main other active international collaborations in 2019 are with:

• E. Deme and A. Diop from Gaston Berger University in Senegal.

• N. Wang and C-C. Tu from University of Michigan, Ann Arbor, USA.

• Guillaume Kon Kam King, Stefano Favaro, Pierpaolo De Blasi, Collegio Carlo Alberto, Turin, Italy.

• Igor Prünster, Antonio Lijoi, and Riccardo Corradin Bocconi University, Milan, Italy.

• Bernardo Nipoti, Trinity College Dublin, Ireland.

• Yeh Whye Teh, Oxford University and DeepMind, UK.

• Stephen Walker, University of Texas at Austin, USA.

• Alex Petersen, University of California Santa Barbara, USA.

• Dimitri van de Ville, EPFL, University of Geneva, Switzerland.