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Overall Objectives
Application Domains
New Results
Bilateral Contracts and Grants with Industry
Bibliography
Overall Objectives
Application Domains
New Results
Bilateral Contracts and Grants with Industry
Bibliography


Section: Partnerships and Cooperations

European Initiatives

FP7 & H2020 Projects

SIPA
  • Title: Semidefinite Programming with Applications in Statistical Learning

  • Type: FP7

  • Instrument: ERC Starting Grant Duration: May 2011 - May 2016 Coordinator: A. d’Aspremont (CNRS)

  • Abstract: Interior point algorithms and a dramatic growth in computing power have revolutionized optimization in the last two decades. Highly nonlinear problems which were previously thought in- tractable are now routinely solved at reasonable scales. Semidefinite programs (i.e. linear programs on the cone of positive semidefinite matrices) are a perfect example of this trend: reasonably large, highly nonlinear but convex eigenvalue optimization problems are now solved efficiently by reliable numerical packages. This in turn means that a wide array of new applications for semidefinite pro- gramming have been discovered, mimicking the early development of linear programming. To cite only a few examples, semidefinite programs have been used to solve collaborative filtering problems (e.g. make personalized movie recommendations), approximate the solution of combinatorial pro- grams, optimize the mixing rate of Markov chains over networks, infer dependence patterns from multivariate time series or produce optimal kernels in classification problems. These new appli- cations also come with radically different algorithmic requirements. While interior point methods solve relatively small problems with a high precision, most recent applications of semidefinite pro- gramming in statistical learning for example form very large-scale problems with comparatively low precision targets, programs for which current algorithms cannot form even a single iteration. This proposal seeks to break this limit on problem size by deriving reliable first-order algorithms for solv- ing large-scale semidefinite programs with a significantly lower cost per iteration, using for example subsampling techniques to considerably reduce the cost of forming gradients. Beyond these algo- rithmic challenges, the proposed research will focus heavily on applications of convex programming to statistical learning and signal processing theory where optimization and duality results quantify the statistical performance of coding or variable selection algorithms for example. Finally, another central goal of this work will be to produce efficient, customized algorithms for some key problems arising in machine learning and statistics.

MacSeNet
  • Title: Machine Sensing Training Network

  • Type: H2020

  • Instrument: Initial Training Network

  • Duration: January 2015 - January 2019

  • Coordinator: Mark Plumbley (University of Surrey)

  • Inria contact: Francis Bach

  • Abstract: The aim of this Innovative Training Network is to train a new generation of creative, entrepreneurial and innovative early stage researchers (ESRs) in the research area of measurement and estimation of signals using knowledge or data about the underlying structure. We will develop new robust and efficient Machine Sensing theory and algorithms, together methods for a wide range of signals, including: advanced brain imaging; inverse imaging problems; audio and music signals; and non-traditional signals such as signals on graphs. We will apply these methods to real-world problems, through work with non-Academic partners, and disseminate the results of this research to a wide range of academic and non-academic audiences, including through publications, data, software and public engagement events. MacSeNet is funded under the H2020-MSCA-ITN-2014 call and is part of the Marie Sklodowska- Curie Actions — Innovative Training Networks (ITN) funding scheme.

Spartan
  • Title: Sparse Representations and Compressed Sensing Training Network Type: FP7

  • Instrument: Initial Training Network

  • Duration: October 2014 to October 2018

  • Coordinator: Mark Plumbley (University of Surrey)

  • Inria contact: Francis Bach

  • Abstract: The SpaRTaN Initial Training Network will train a new generation of interdisciplinary researchers in sparse representations and compressed sensing, contributing to Europe’s leading role in scientific innovation. By bringing together leading academic and industry groups with expertise in sparse representations, compressed sensing, machine learning and optimisation, and with an interest in applications such as hyperspectral imaging, audio signal processing and video analytics, this project will create an interdisciplinary, trans-national and inter-sectorial training network to enhance mobility and training of researchers in this area. SpaRTaN is funded under the FP7-PEOPLE-2013-ITN call and is part of the Marie Curie Actions — Initial Training Networks (ITN) funding scheme: Project number - 607290

SEQUOIA
  • Title: Robust algorithms for learning from modern data

  • Programm: H2020

  • Type: ERC

  • Duration: 2017-202

  • Coordinator: Inria

  • Inria contact: Francis BACH