Section: Partnerships and Cooperations

National Initiatives


Participants : Mohammad Ghavamzadeh, Jérémie Mary, Olivier Nicol, Philippe Preux, Daniil Ryabko.

  • Title: Learning Algorithms, Models and sPArse representations for structured DAta

  • Type: National Research Agency (ANR-09-EMER-007)

  • Coordinator: Inria Lille – Nord Europe (Mostrare)

  • Others partners: Laboratoire d'Informatique Fondamentale de Marseille; Laboratoire Hubert Curien à Saint Etienne; Laboratoire d'Informatique de Paris 6.

  • Web site: http://lampada.gforge.inria.fr/

  • Duration: ends mid-2014

  • Abstract: Lampada is a fundamental research project on machine learning and structured data. Lampada focuses on scaling learning algorithms to handle large sets of complex data. The main challenges are 1) high dimension learning problems, 2) large sets of data and 3) dynamics of data. We consider evolving data. The representation of these data involves both structure and content information and are typically large sequences, trees and graphs. The main application domains are web2, social networks and biological data.

    The project proposes to study formal representations of such data together with incremental or sequential machine learning methods and similarity learning methods.

    The representation research topic includes condensed data representation, sampling, prototype selection and representation of streams of data. Machine learning methods include edit distance learning, reinforcement learning and incremental methods, density estimation of structured data and learning on streams.

  • Activity Report:

    Philippe Preux has collaborated with Ludovic Denoyer and Gabriel Dulac-Arnold from LIP'6 to investigate further the idea of datum-wise representation, introduced in 2011.

    Mohammad Ghavamzadeh and Philippe Preux have collaborated with Hachem Kadri on an operator-based approach for structured output [15] .

    Daniil Ryabko has developed a theory for unsupervised learning of time-series dependence, where the time series are either coming from a stationary environment or are a result of interaction with a Markovian environment with a continuous state space. Danil Ryabko and Jeremie Mary have developed methods for using binary classification methods for solving various unsupervised learning problems about time series.


Participant : Rémi Munos.

  • Title: Brain computer co-adaptation for better interfaces

  • Type: National Research Agency (ANR-09-EMER-002)

  • Coordinator: Maureen Clerc

  • Other Partners: Inria Odyssee project (Maureen Clerc), the INSERM U821 team (Olivier Bertrand), the Laboratory of Neurobiology of Cognition (CNRS) (Boris Burle) and the laboratory of Analysis, topology and probabilities (CNRS and University of Provence) (Bruno Torresani).

  • Web site: https://twiki-sop.inria.fr/twiki/bin/view/Projets/Athena/CoAdapt/WebHome

  • Duration: 2009-2014

  • Abstract: The aim of Co-Adapt is to propose new directions for BCI design, by modeling explicitly the co-adaptation taking place between the user and the system. The goal of CoAdapt is to study the co-adaptation between a user and a BCI system in the course of training and operation. The quality of the interface will be judged according to several criteria (reliability, learning curve, error correction, bit rate). BCI will be considered under a joint perspective: the user's and the system's. From the user's brain activity, features must be extracted, and translated into commands to drive the BCI system. From the point of view of the system, it is important to devise adaptive learning strategies, because the brain activity is not stable in time. How to adapt the features in the course of BCI operation is a difficult and important topic of research. We will investigate Reinforcement Learning (RL) techniques to address the above questions.

  • Activity Report: The performances of a BCI can vary greatly across users but also depend on the tasks used, making the problem of appropriate task selection an important issue. We develop an adaptive algorithm, UCB-classif, based on the stochastic bandit theory. This shortens the training stage, thereby allowing the exploration of a greater variety of tasks. By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. See [4] and https://twiki-sop.inria.fr/twiki/bin/view/Projets/Athena/CoAdapt/WebHome


Participant : Pierre Chainais.

  • Title: Multifractal Analysis and Applications to Signal and Image Processing

  • Type: National Research Agency

  • Coordinator: Univ. Paris-Est-Créteil (S. Jaffard)

  • Duration: 2011-2015

  • Other Partners: Univ. Paris-Est Créteil, Univ. Sciences et Technologies de Lille and Inria (Lille), ENST (Telechom ParisTech), Univ. Blaise Pascal (Clermont-Ferrand), and Univ. Bretagne Sud (Vannes), Statistical Signal Processing group at the Physics Department at the Ecole Normale Supérieure de Lyon, one researcher from the Math. Department of Institut National des Sciences Appliquees de Lyon and two researchers from the Laboratoire d'Analyse, Topologie et Probabilités (LAPT) of Aix-Marseille University.

  • Abstract: Multifractal analysis refers to two concepts of different natures: On the theoretical side, it corresponds to pointwise singularity characterization and fractional dimension determination ; on the applied side, it is associated with scale invariance characterization, involving a family of parameters, the scaling function, used in classification or model selection. Following the seminal ideas of Parisi and Frisch in the mid-80s, these two components are usually related by a Legendre transform, stemming from a heuristic argument relying on large deviation and statistical thermodynamics principles: The multifractal formalism. This led to new theoretical approaches for the study of singularities of functions and measures, as well as efficient tools for classification and models selection, that allowed to settle longstanding issues (e.g., concerning the modeling of fully developed turbulence). Though this formalism has been shown to hold for large classes of functions of widely different origins, the generality of its level of validity remains an open issue. Despite its popularity in applications, the interactions between theoretical developments and applications are unsatisfactory. Its use in image processing for instance is still in its infancy. This is partly due to discrepancy between the theoretical contributions mostly grounded in functional analysis and geometric measure theory, and applications naturally implying a stochastic or statistical framework. The AMATIS project aims at addressing these issues, by proposing a consistent and documented framework combining different theoretical approaches and bridging the gap towards applications. To that end, it will both address a number of challenging theoretical issues and devote significant efforts to elaborating a WEB platform with softwares and documentation. It will combine the efforts of mathematicians with those of physicists and experts in signal and image processing. Dissemination among and interactions between scientific fields are also intended via the organization of summer schools and workshop.

  • Activity Report: a collaboration with P. Bas (CR CNRS, LAGIS) deals with the steganalysis of textured images. While steganography aims at hiding a message within some support, e.g. a numerical image, steganalysis aims at detecting the presence or not of any hidden message in the support. Steganalysis involves two main tasks: first identify relevant features which may be sensitive to the presence of a hidden message, then use supervised classification to build a detector. While the steganalysis of usual images has been well studied, the case of textured images, for which multifractal models may be relevant, is much more difficult. Indeed, textured images have a rich and disordered content which favors hiding information in an unperceptible manner. A student internship of 8 months at Master level in 2012 has led us to consider a very fundamental question. Steganalysis is usually proceeded to a classification based on histograms of features (bag of words). We consider the problem of the optimization of the bins of such histograms with respect to the performance of the classifier. We have shown that a balanced version of K-means which fills each cell equally yields an efficient quantization to this respect [28] .

National Partners

  • Laboratoire de Mathématiques d'Orsay, France.

    • Mylène Maïda Collaborator

      Ph. Preux has collaborated with M. Maïda and co-advised a student of the École Centrale de Lille. The motivation of this collaboration is the study of random matrices and the potential use of this theory in machine learning.

  • LIF - CMI - Université de Provence.

    • Julien Audiffren Collaborator

      M. Valko, A. Lazaric, and M. Ghavamzadeh work with Julien on Semi-Supervised Apprenticeship Learning. We have recently developed a maximum entropy algorithm that outperforms the approach without unlabeled data.

  • Laboratoire Lagrange, Université de Nice, France.

    • Cédric Richard Collaborator

      We have had collaboration on the topic of dictionary learning over a sensor network. We have published 2 conference papers [29] and [10] .

  • Laboratoire de Mécanique de Lille, Université de Lille 1, France.

    • Jean-Philippe Laval Collaborator

      We co-supervise a starting PhD student (Linh Van Nguyen) on the topic of high resolution field reconstruction from low resolution measurements in turbulent flows.

  • Biophotonics team at the Interdisciplinary Research Institute (IRI), Villeneuve d'Ascq, France.

    • Aymeric Leray Collaborator

      We have co-supervised an intern student (Pierre Pfennig, 2 months) on the topic of quantitative guarantees of a super resolution method via concentration inequalities. A paper is submitted to ICASSP 2014.

  • LAGIS, Ecole Centrale Lille - Université de Lille 1, France.

    • Patrick Bas Collaborator

      We have a collaboration on the topic of adaptive quantization to optimize classification from histrograms of features with an applicaiton to the steganalysis of textured images.