Team : AMA

Transcription

Team : AMA

Team: AMA
Scientific leader: E. Gaussier, M. B. Gordon
Project for the creation of a team
Apprentissage : Modèles & Algorithmes (AMA)
(Machine Learning: Models & Algorithms)
Web site: http://equipes-liglab.imag.fr/
Parent Organizations: Université Grenoble 1, Université Grenoble 2, Grenoble INP, CNRS
Contents
1
General presentation
2
2
Team Composition
2
3
Research Themes
3.1 Fundamentals of machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Analysis and exploration of complex data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Learning and social systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
4
5
6
4
Application Domains and Social, Economic or Interdisciplinary Impact
6
5
Contracts and Grants
5.1 External Contracts and Grants (Industry, European, National) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Research Networks (European, National, Regional, Local) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Internal Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
7
7
7
6
Principal International Collaborations
8
7
Visibility, Scientific and Public Prominence
7.1 Contribution to the Scientific Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Prizes and awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 Public Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
8
10
10
8
Software and Research Infrastructure
10
9
Educational Activities
10
10 Self-Assessment
10.1 Self-positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 SWOT analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
11
12
11 Perspectives for the Research Team
13
12 Publications
14
This document presents the creation of a new team within the LIG. This new team relies on 6 permanent individuals with
complementary expertise. These individuals have collaborated at various degrees in the past years (through informal and
formal projects, through the coordination of teaching activities in machine learning/data analysis and through the organization
of events) and share a common interest for machine learning and data analysis. We believe that the time is ripe now for
integrating our various expertise and common interests into a single team within the LIG, for the following reasons:
1. Our research area is anchored in theoretical computer science (how to make machines learn from, reason on and adapt to
their environment) with applications in many different domains related to several LIG teams: robotics, natural language
processing, information retrieval, computer vision, image processing;
2. Our research agenda directly addresses some of the major challenges on which the LIG is positioned, as (we give in
parentheses the associated LIG challenge; see the LIG project for the 2011-2014 quadrennial):
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• Large scale information access (conceptual challenge: Information Access)
• Modeling of complex, dynamic and structured data (conceptual challenge: Information Access)
• Analysis and modeling of social systems (societal challenge: Open Enterprise)
3. Our team rallies and merges activities that were previously spread over different teams and labs, and offers the opportunity for a strong positioning around machine learning, data analysis and social systems.
The remainder of this document is organized as follows. We first provide in sections 1, 2 and 3 the composition of the
envisaged team and the research axes we want to develop. We then give in sections 4 to 9 general background on the team
members and their activities as researchers and teachers. These sections should not be read as a summary of the past activity
of a team, but as a summary of the past activity of individuals, the goal here being to illustrate the fact that we are already
engaged in several scientific and contractual activities which will benefit to the team. Sections 10 and 11 provide a positioning
of the team within the LIG and summarize the research activity we want to develop. Lastly, section 12 lists the publications
of the team members. Here again, the goal is not to present a summary of a team’s publication activity, but to provide an idea
of the publications of the individual members of the envisaged team.
1
General presentation
Machine learning as a research field appeared at the end of 50s, when one came to the conclusion that it was impossible to
manually encode, within artificial intelligence applications, all the information and knowledge necessary to solve practical
problems. Right from the start, two main research tracks were developed: (a) an activity oriented towards modeling and
inference of decision functions, which corresponds to what is currently referred to as machine learning and which is close
to data analysis, and (b) an activity aiming at studying cognitive mechanisms at play during a learning activity, and which
is close to cognitive sciences. Nowadays, depending on the information to be extracted and the application targeted, several
paradigms allow one to cluster (unsupervised learning) or discriminate (supervised, semi-supervised, transductive learning)
vectorial or relational data, in a static or dynamic way (active learning), or even pilot complex systems (reinforcement learning). In each of these paradigms, several learning methods are called for in order to analyze data and build models. These
methods relate to logics (e.g. inductive logic programming), statistics (e.g. support vector machines, hidden Markov models),
data analysis (factorial analysis), biology (evolutionary algorithms) or statistical physics (Boltzmann machines). A distinction
is traditionally made between symbolic and numerical methods, depending on their reliance on discrete or continuous models.
In practice, this distinction operates as a trade-off between interpretability and effectiveness of the model learnt (the notion of
interpretability is related to the fact that a user can easily comprehend the knowledge inferred by a certain model). Machine
learning has been so popular in the past years that it has been applied to many different domains, to name but a few: social network analysis, Web usage mining, text clustering and categorization, ontology learning, bio- and chemo-informatics, decision
making, robotics, pattern recognition, ...
The AMA team
The overall goal of the AMA team is to design methods and models for the adaptation of a system to its environment, based
on data collected from this environment. Our research thus fits within the two axes mentioned previously: (a) inference of
decision functions and (b) computational models of the behavior of individuals and groups of individuals. In the first axis, we
will focus on structured and dynamic data, as graphs or networks evolving over time (most social networks and RSS flows can
be formalized in this way). Several methods have been developed for learning from structured or dynamic data, but very few
ones have been proposed for both structured and dynamic data. In the second axis, we will focus on the modeling of cognitive
processes at play when several social agents interact, in both economic and sociological domains. This research line, which
has connections with the previous one, is thus at the intersection of machine learning, cognitive sciences and the study of
complex systems.
Our will to develop these two aspects of machine learning, while bridging them through the study of complex and structured
data/systems evolving over time, is a key differentiator of our team with respect to other machine learning teams in France
and abroad.
2
Team Composition
The AMA team will rely on 6 permanent individuals with complementary expertise:
• Cécile Amblard, MCF, UJF, currently within the team TIMB, TIMC lab, bringing in competencies in applied mathematics, and especially statistics (26th section, CNU);
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• Gilles Bisson, CR CNRS, currently within the team AMA, TIMC lab, bringing in competencies in machine learning
and artificial intelligence (7th section, CNRS);
• Ahlame Douzal, MCF UJF, currently within the team TIMB, TIMC lab, bringing in competencies in data analysis and
machine learning (27th section, CNU);
• Catherine Garbay, DR CNRS, currently within the team MAGMA, LIG lab, bringing in competencies in artificial
intelligence and cognitive sciences (7th section, CNRS);
• Eric Gaussier, PR UJF, currently within the team MRIM, LIG lab, bringing in competencies in machine learning and
information access (27th section, CNU);
• Mirta Gordon, DR CNRS, currently within the team AMA, TIMC lab, bringing in competencies in statistical physics
and cognitive science (7th section, CNRS).
The envisaged team is thus composed of two persons at the heart of the current AMA team existing in the TIMC lab (G. Bisson
and M. B. Gordon), two additional members of the TIMC lab (C. Amblard and A. Douzal), and two members from the LIG
(C. Garbay and E. Gaussier). In addition, several PhD students are currently supervised by one (or several) team members.
Some of these students have just started their PhD, and will be part of the new team, provided, of course, it is created. Others
will have finished before such a creation is made possible. We mention them here anyway as this gives an indication of the
number of PhD students we want to have, in a continuous flow.
Doctoral Students
Name
University
Supervisors
Funding (sources and dates)
Date of first
registration
S. Clinchant
UJF
E. Gaussier
CIFRE
09/11)
Sept. 08
A. Diallo
UJF
C. Frambourg
UJF
C. Grimal
UJF
F. Hussain
UJF
C. Lagnier
UJF
B. Li
UJF
C. Metzig
UJF
M. B. Gordon
F. Meyer
UJF
E. Gaussier
J.-F. Patri
UJF
A. M. Qamar
UJF
A. Douzal (co-supervision F.
Giroud)
A. Douzal (co-supervision J.
Demongeot)
G. Bisson (co-supervision E.
Gaussier)
G. Bisson (co-supervision M. B.
Gordon)
E. Gaussier (co-supervision G.
Bisson)
E. Gaussier (co-supervision J.P. Chevallet)
M. B. Gordon (co-supervision
J.-P. Nadal)
E. Gaussier
(XRCE;
09/08-
None
Sept. 2005
MENRT (09/08-09/11)
Sept. 08
ANR (02/09-02/12)
Nov. 09
Allocation from Pakistan (as
par of the French-Pakistan
scientific collaboration)
Jan. 07
MENRT (10/09-10/12)
Oct. 09
ANR (02/09-02/12)
April 09
Allocation from EDIS doctoral school in Cognitive
Science (04/10-04/13)
Orange Labs (full time employee; 33% of his time devoted to his PhD; 03/0803/11)
Apil 10
March 08
DGA (10/09-10/12)
Oct. 09
MENRT (11/07-11/10)
Nov. 07
Lastly, 8 PhD theses, supervised by one or several members of the team, were successfully defended during the current
quadrennial (J. Bourbeillon, T. Guyet, L. Kefi, D. Renaudie, V. Robinet, B. Scherrer, V. Semeshenko, S. Wieczorek).
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3
Research Themes
The research we want to develop within the AMA team can be structured into three main themes, which can in turn be
decomposed into theoretical and practical axes.
1. Theme 1: Fundamentals of machine learning
• Axis: Proximity indices and metrics for complex data
• Axis: Phase transition in learning
• Axis: Learning in high dimensions
2. Theme 2: Analysis and exploration of complex data
• Axis: Classification, clustering and (co-)clustering
• Axis: Visualization of relational data
3. Theme 3: Learning and social networks
• Axis: Social systems of learning individuals
• Axis: Agent based dynamic learning
3.1
Fundamentals of machine learning
This theme is devoted to the the study of three fundamental problems in machine learning, which have impacts on the following
research themes.
Axis: Proximity indices and metrics for complex data
(Persons involved: C. Amblard, G. Bisson, A. Douzal, E. Gaussier, M. B. Gordon)
Proximity indices and metrics play a crucial role in learning as they are at the basis of similarity and distance measures used
to represent datasets (many classification methods directly take as input a similarity or dissimilarity matrix). We first want
to study in this axis the different measures proposed for relational and temporal data so as to (a) if possible, unify them
within a common framework, (b) compare them from first principles, (c) be able to chose the appropriate measure for a
targeted application or a given goal, and (d) extend the methods used for temporal relations to other contiguity relations (as
spatial relations). We also want to study the learning of metrics appropriate to the data under focus, i.e. metrics reflecting
the underlying geometry of the data. There have been a lot of works on metric learning, aiming at learning an appropriate
distance function. We want to extend these works so as to learn appropriate similarity functions (e.g. generalized cosines) as
similarity functions are sometimes preferred over distance functions. Lastly, we plan on studying inverse methods associated
with Bayesian modeling so as to derive new dissimilarity functions.
Axis: Phase transition in learning
(Persons involved: G. Bisson, M. B. Gordon)
In ILP (Inductive Logic Programming), learning often relies on algorithms of the type generate and test. A major component
of such algorithms is the coverage test, based on θ-subsumption, which yields a yes/no answer: Either the current hypothesis
covers the example, or it does not. Several researchers have shown that the use of θ-subsumption was associated with a phase
transition phenomenon, rendering the generate and test approaches inadequate for some learning problems. In this context,
we plan to generalize the standard coverage test used in ILP through the introduction of the notion of partial coverage between
an hypothesis and an example. More precisely, given two clauses, h and e, we want (a) to determine whether a sub-clause h0
of h covers e, and (b) to quantify the degree, as a real value, of the partial subsumption. This work thus aims at proposing new
learning methods in ILP.
Axis: Learning in high dimensions
(Persons involved: C. Amblard, A. Douzal, E. Gaussier)
The applications we are targeting often involve high dimensional data. As an illustration, DMOZ (www.dmoz.org), the largest
repository of the web, contains hundred of thousands of categories, hierarchically organized, and several million documents
lying in a vector space of hundred of thousands of dimensions. It is not possible to directly deploy existing methods in such
datasets, and one needs to revisit existing techniques through parallelism, model simplification and sampling (e.g. through
the use of few annotated data and semi-supervised, transductive or active learning). More specifically, we want to address the
following problems: Can one define criteria allowing, without resorting to classification, to compare two category systems
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so as to select the best one? Can such criteria be used to infer hierarchical category systems? Can one learn parsimonious
models, leading to faster algorithms, behaving well in high dimensions, a situation where non-parsimonious models are
usually required? Part of these problems will be addressed first within the family of decision functions learned via regularized
maximum likelihood, a family for which consistency results and limiting distributions should help establish the criteria we are
looking for.
3.2
Analysis and exploration of complex data
This theme is devoted to the development of models and algorithms for analyzing and visualizing complex data. It focuses on
certain data types and methods.
Axis: Classification, clustering and co-clustering
(Persons involved: C. Amblard, G. Bisson, A. Douzal, E. Gaussier, M. B. Gordon)
Members of the team have a strong experience and expertise in classification (supervised learning) and clustering (unsupervised learning). We want to extend this experience and expertise to new types of data and to new applications.
• Classification of temporal data More and more applications involve variables evolving over time, for which it is necessary
to develop solutions for classification and regression. We are particularly interested in this area in extending decision
and regression trees, as one of our goals is to produce interpretable models, which is the case for the models associated
with classification and regression trees. This problem has only recently been addressed, and very few results have been
obtained. The approach we want to follow relies on two main elements: (a) proximity indices and metrics for temporal
data as described in the previous theme (research theme 1), adapted to the particular case of discrimination, and (b) the
identification of highly discriminant sub-sequences. This last point is crucial and opens the way to feature set selection
with contiguity constraints.
• Clustering of chemical data Relational data is present in many domains under various forms (logical expressions, graphs,
object representations, ...). The targeted application domain here is chemo-informatics, and more particularly the study
of screening data. High throughput screening of chemo-libraries is a standard experimental approach to discover effectors. In this context, it is important to provide chemists with tools to help them explore the content of chemo-libraries
and to organize (semi-)automatically this content so as to better localize active molecules and better evaluate the diversity of chemical structures. We want to develop here new clustering tools based on new proximity indices and metrics,
as the ones explored in the previous theme (research theme 1).
• Co-clustering Co-clustering is a data mining technique which allows simultaneous clustering of the rows (e.g. individuals) and columns (e.g. variables or features) of a matrix (typically a collection matrix). This definition can be
extended to the case where the data is not only represented by a matrix, but by a set of matrices or tensors (defined
here as multi-dimensional extensions of matrices). Several members of the team have worked on different co-clustering
models, sometimes referred to as co-occurrence models: LSA (Latent Semantic Analysis), PLSA (Probabilistic Latent
Semantic Analysis), NMF (Non-negative Matrix Factorization) and χ-Sim (developed by G. Bisson and his students).
All these models were originally defined for matrices, but extensions to tensors begin to appear. We plan on revisiting all
these models in the case of both set of matrices and tensors, which raise different generalization problems. In particular,
we would like to explore the links between generalizations of NMFs and χ-Sim with probabilistic models for n-uples.
Some of us were the first ones to establish a formal relation between PLSA and NMF, and we plan to follow the same
approach for generalized versions of these techniques.
Axis: Data visualization
(Person involved: G. Bisson)
Hierarchical clustering is a valuable tool for analyzing and modeling data. However, when one works with dendrograms
containing more than a few hundred leaves, it becomes difficult to visualize and explore the tree. It is indeed possible to
visualize only part of the data (e.g. through zooming on a particular zone), but one lacks, in doing so, a global view of the
data. One of us has introduced a new model for visualizing dendrograms (a model called Stacked Trees) allowing one to
simultaneously represent the information contained in tens of thousands of objects. This work was proposed in the framework
of chemical data analysis and needs be extended to cover other data types, as texts or temporal data. We plan to investigate
such an extension within the new AMA team.
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3.3
Learning and social systems
This theme is devoted to the study of social systems so as to (a) propose accurate, computational models of such systems, and
(b) be able to deploy learning algorithms when a complex system interacts with a dynamically evolving environment. The
research we plan to develop within the second theme (Analysis and exploration of complex data) on co-clustering also fits
within this theme as it is partly concerned with accessing information in social networks. We do not repeat it here.
Axis: Social systems of learning individuals
(Persons involved: G. Bisson, M. B. Gordon)
This axis aims at studying the passage from individual to collective behaviors (how to model a community of individuals from
a model of each individual?) as well as the macroscopic dynamics of social systems (can we elaborate models accounting
for the evolution of social systems?). This question is crucial from a theoretical point of view, but also from a societal
one as it can impact the definition of public policies. From a theoretical point of view, we want to go beyond models with
complementary interactions (as Schelling’s and Granovetter’s models), where individuals tend to behave in conformity with
others (positive interactions), as such models do not account for all the situations encountered in practice. Our goal here is
to propose other models to account for both positive and negative interactions (as in fashion), non-symmetrical interactions
and interactions mediated through larger networks (as for criminals). The use of models derived from dynamic systems and
statistical physics, as well as the use of numerical simulation, should allow the development of social models accounting for
the various phenomena observed in practice. Furthermore, additional clustering and co-clustering methods can also be used
to study certain types of data, as the ones pertaining to criminality.
Axis: Agent based dynamic learning
(Persons involved: C. Garbay, E. Gaussier)
The general context here is the one of autonomous systems interacting with a complex environment, for which the learning
problem involves heterogeneous processes (human and artificial agents, constructivist and classification-based approaches,
...). Furthermore, in many situations, the available annotated data does not cover all the situations one can face (as in image
processing). Having agents collaborating to dynamically construct partial yet complementary solutions to the overall problem
thus seems a promising direction. In particular, agents can collaborate to incrementally construct a complete solution gradually
covering all the data types and situations. The learning framework one faces here has the following characteristics:
• It is multi-scale as individuals as well as communities of agents are considered;
• It calls for learning paradigms dedicated to situations where few annotated data is available, as:
– semi-supervised or transductive learning, which allows one to leverage small amount of annotated data with large
amount of unannotated data,
– and active learning, which allows the dynamic annotation of data so as to improve the solution learned so far;
• It is contextual and collaborative (as agents interact to construct partial solutions which depend on the situations considered).
The goal of learning in this framework is thus to articulate machine learning methods within a complex environments where
solutions can only be built incrementally, by gradually covering more and more situations, in a dynamic, evolving environment.
4
Application Domains and Social, Economic or Interdisciplinary Impact
The study of complex, dynamic systems through computational models has expanded to social sciences, and the modeling
of the interactions between individuals has become the focus of several research lines. Within this field, we are interested
in the modeling of systems of social agents, evolving over time and interacting to achieve a certain goal, be it the one of
sharing information or fostering collaborations. Mining such systems so as to discover recurrent or emerging patterns will
help identify communities and outliers, as well as predict their evolution over time. This research area, related to the third
research theme presented in the previous section, has a direct societal impact, inasmuch as computationl models and software
one can develop could be used to reduce criminality and help peolple interacting in smoother ways.
On more traditional aspects, the research we envisage on large-scale classification could directly benefit to patent offices,
to companies involved in intellectual property management or to organizations maintaining large category systems (as the
DMOZ association). Similarly, the research related to the second theme of section 3, Analysis and exploration of complex
data, can lead to new tools for exploiting data streams, as RSS flows. Such tools can have a direct economic impact, either
through existing companies (TEMIS, MatrixWare, ...) or through start-ups to be created.
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5
5.1
Contracts and Grants
External Contracts and Grants (Industry, European, National)
• [Industry]] Contract with Orange Labs on machine learning techniques for recommendation systems; 3 years, from
Feb. 2008 till Feb. 2011 (15ke)
• [Industry]] Contract with Xerox Research Centre Europe on machine learning for information access in connected
collections; 3 years, from June 2008 till June 2011 (13ke)
• [Industry] Contract with THEMIS on survival analysis of clinical data; 6 months (ca. 8ke)
• [European] Resarch collaboration with the Information Retrieval Facility (IRF) on Large Scale Text Classification.
This collaboration is not associated with funding but allows us to use part of the computing infrastructure available at
the IRF in Vienna, Austria
• [ACI IMPIO (past)] ACCAMBA project on the design of tools for analyzing chemolibraries; 3 years, from Sept. 2004
till Sept. 2007 (90ke)
• [ANR] METRICC project on exploitation of comparable corpora for bilingual information access; 3 years, from Feb.
2009 till Feb. 2012 (ca. 150ke)
• [ANR] FRAGRANCES project on mining and learning from social networks; 3 years, from Feb. 2009 till Feb. 2012
(ca. 120ke)
• [ANR] SYSCOMM DyXi project on dynamics of social groups; 3 years, from Feb. 2009 till Feb. 2012. Our participation to this project is administered by ENS Paris (for operation costs) and EHESS (for personnel)
• [ARC-INRIA] Selmic project on longitudinal and mulimodal segmentation of cerebral abnormalities with MRI; 3
years, 2007-2009 (ca. 70ke)
• [DGA] Project on the physiological follow-up of a warrior; 3 years, from July 2009 till July 2012 (ca. 120ke)
5.2
Research Networks (European, National, Regional, Local)
• [European NoE] Several team members are part of the Network of Excellence PASCAL (Pattern Analysis, Statistical
Modeling and Computational Learning). This Network of Excellence allows us to benefit from funding to attend
conferences and set up challenges, as the Large Scale Hierarchical Text Classificaiton Challenge we co-organized in
2009 (with researchers from Demokritos, Athens)
• [Regional project] Several team members are part of the CLUSTER project Web Intelligence
• [Regional project] IXXI project on modellization of criminality; 2008 (ca; 5ke)
5.3
Internal Funding
• [MSTIC project] LASCAR project on LArge Scale CAtegoRization; 2 years, from Jan. 2008 till Dec. 2009 (10ke
plus funding for a one year post-doc)
• [MSH project] (past)] Project on economical analysis of the dynamics of social norms; 28 months, 2004-2006 (ca.
5.3ke)
• [IPI project] (past)] Project on efficient coordination with sub-optimal choices; 2 years, 2005-2006 (ca. 5ke)
• [BQR project] (past)] Project on human cooperation, BQR INPG, 2006 (ca; 5ke)
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6
Principal International Collaborations
In addition to some of the international projects mentioned previously, we have collaborations with researchers in different
countries, on different research themes, including exploratory data analysis, proximity measures, information retrieval and
large scale classification. These collaborations involve joint publications and/or joint organization of events, with funding. In
particular: A. Douzal has collaborations with L. Billard from Georgia University (USA) and P. N. Nagabhushan from Mysore
University (India). E. Gaussier has collaborations with J. Savoy from Neuchâtel University (Swiss), G. Paliouras from NCSR
Demokritos (Greece) and J.-C. Chappelier from EPFL (Swiss).
7
7.1
Visibility, Scientific and Public Prominence
Contribution to the Scientific Community
Management of Scientific Organizations
• M. B. Gordon, co-director of the Laboratoire Leibniz (2004-2006)
• M. B. Gordon member of the Scientific Committee of the Institut des Sciences de la Complexité Paris-Ile-de France,
since 2006
• M. B. Gordon member of the Scientific Committee of IXXI (Institut des Sciences de la COmplexité de Lyon), since
2007
• C. Garbay, director of the CLIPS laboratory (2006)
• C. Garbay, co-director of the Laboratoire LIG (since 2006)
• C. Garbay member of the scientific council of the Institut Géographique National
• C. Garbay member of the scientific council of the MSH Paris Nord
• C. Garbay member of the Comité de Pilotage de l’Institut des Sciences de la Communication (CNRS)
• C. Garbay member of the Comité de Pilotage de l’ARP ANR PIRSTEC (Prospective Interdisciplinaire en Réseau pour
les Sciences et TEchnologies Cognitives)
Administration of Professional Societies
• E. Gaussier member of the European Research Council, Computer Science panel, for Starting Grants, since 2006
• E. Gaussier member of the Executive board of the Euroapean Association for Computaitonal Linguistics, since 2007
• M. B. Gordon member of the Administrative Board of the Association pour la Recherche Corgnitive (ARCo), since
2006
Editorial Boards
• Computational Linguistics, E. Gaussier, 2005-2007
• Traitement Automatique des Langues, E. Gaussier, 2006-2008
• AIM Journal Artificial Intelligence in Medecine (advisory board), C. Garbay, 1997-2009
• Revue I3 Information-Interaction-Intelligence (founder - Chief Editor), C. Garbay, 2001-2007
Organization of Conferences and Workshops
• Co-presidency of the organization committee of AFIA 2007 (conference from the Association Française d’Intelligence
Artificielle)
• Co-presidency of the organization committee of the 1ière Ecole d’Eté Web Intelligence, Autrans, July 2008
• Co-presidency of the organization committee of the 2ième Ecole d’Automne sur la Recherche d’Information et ses
Applications, Toulouse, Oct. 2008
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• Co-presidency of the organization committee of the LREC 2008 Workshop on Comparable Corpora, Marrakech, Maroc,
2008
• Presidency of the organization committee of the XVIièmes Rencontres de la Société Française de Classification, Sept.
2009
Program Committee Members
• Co-presidency of the program committee of the International Conference on Empirical Methods in Natural Language
Processing, EMNLP’2006 (E. Gaussier)
• Presidency of the program committee of the INTECH seminar, INRIA Rhône-ALpes, Jan. 2008 (E. Gaussier)
• International Conference on Information Retrieval (ECIR), E. Gaussier, 2006-2009
• European Conference on Information Retrieval (ECIR), E. Gaussier, 2006-2009
• European Conference on Computational Linguistics (EACL), E. Gaussier, 2006
• International Conference on Computational Linguistics (ACL), E. Gaussier, 2006
• European Conference on Machine Learning (ECML), E. Gaussier, 2007, 2009
• Conférence sur la Recherche d’Information et ses Applications (CORIA), E. Gaussier, 2006-2009
• Conférence d’Apprentissage (CAp), E. Gaussier, 2009
• European Symposium on Artificial Neural Networks, M. B. Gordon, 2003-2007
• Colloques ARCo, M. B. Gordon, 2006-2008
• Computational Methods for Modeling and leArning in Social and Human Sciences (MASHS), M. B. Gordon, 20072009
• International Conference on Tools with Artificial Intelligence (ICTAI), G. Bisson, 2008
• International Conference on Advanced Computing and Communication, A. Douzal, 2005
• International Conference on Pattern Recognition and Machine Intelligence, A. Douzal, 2005
• 15ièmes Rencontres de la Société Française de Classification, A. Douzal, 2008
International Expertise
• National Swiss Foundation, E. Gaussier, 2007, 2009
• Fonds de recherche sur la nature et les technologies, Québec, M. B. Gordon, 2005
• CyT, Agentina, M. B. Gordon, 2005
• CERG - Hong-Kong Research Grants Council, M. B. Gordon, 2005, 2007, 2008
National Expertise
• ANR, Masse de données et Contenus et Interactions, E. Gaussier, 2006, 2008, 2009
• Pôle DIGITEO, E. Gaussier, 2008
• AERES (présidence comité d’évaluation), E. Gaussier, 2007
• Member of the evaluation board, ANR Techlog, C. Garbay, 2005-2006
• Presidency of the evaluation committee of the ANR program DEFI, C. Garbay, 2006
• Member of the evaluation committee of the ANR program Communication, C. Garbay
• Member of the Comité National (CNRS), C. Garbay, 2008-2012
Page 9 on 22
• Member then Head of the ASTI Thesis Award committee, C. Garbay, 2005-2009
• Member and Head of Laboratory Evaluation Board (CNRS), C. Garbay, 2006
• Head of Laboratory Evaluation Board, IGN, C. Garby, 2004-2008
• Member of Laboratory Evaluation Board, Institut Telecom, C. Garbay, 2008
7.2
Prizes and awards
Best student paper award:
B. Scherrer, F. Forbes, C. Garbay, M. Dojat. Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation. Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention,
Berlin, 2008.
7.3
Public Dissemination
M. B. Gordon: Interview (50mns) on Radio RCF 103.7 FM Mathématiques et sciences sociales. Broadcasted within Les
Chemins de la Connaissance on Monday, May 29, 2006, and Saturday, June 3, 2006
Within L’Université Populaire de Grenoble, the aim of which is to organize seminars open to everybody
(http://fr.wikipedia.org/wiki/Universite populaire de Grenoble):
• G. Bisson
– Intelligence Artificielle : du mythe à la réalité. 8 March 2007
– Apprentissage du raisonnement en Intelligence Artificielle. 3 May 2007
– Hasard et nécessité des normes industrielles en Informatique. 28 May 2008
• M. B. Gordon
– Les réseaux de neurones (29-03-07)
– De l’apprentissage artificiel à l’apprentissage humain : comment rendre compte des paradoxes sociaux ? (13-0607)
8
Software and Research Infrastructure
Nothing in particular to report here.
9
Educational Activities
Supervision of Educational Programs
• E. Gaussier: director of the Master Informatique de l’UJF (since Sept. 2008)
• E. Gaussier: director of the Magistère Inforamtique de l’UJF (Sept. 2006-Sept. 2008)
Teaching
Name
Position
C. Amblard
MDC UJF
G. Bisson
CR CNRS
A. Douzal
MDC UJF
Year
20062009
20062009
20062009
Number
of
hours to teach
Academic Program
University
192/year
Licence, M1, M2
UJF
30/year
Licence,
CUEFA
218/year
Licence, M1, M2
M2,
Cycle C
UJF
UJF
Page 10 on 22
E. Gaussier
PR UJF
M. B. Gordon
DR CNRS
10
20062009
20062009
210/year
Licence, M1, M2
UJF
15/year
M2, Ecole doctorale
UJF
Self-Assessment
The creation of a new team within a laboratory is a challenging task, one aspect of which lies in positioning the new team
within the existing research landscape. The axes we plan on developing intersect the research axes of other teams within
the LIG. We first detail here our positioning within the LIG, as well as the possible collaborations with teams which lead a
research complementary to ours, prior to provide a SWOT analysis of the creation of the AMA team in the LIG.
10.1
Self-positioning
The E-MOTION team conducts research on models and algorithms for the construction of intelligent systems. Three main
axes are developed: (1) Perception and multimodal modeling of space and movement, (2) movement planning and autonomous
navigation, and (3) Bayesian models for learning, decision making and sensory-motor systems. This last axis shares several
aspects with AMA’s research axes. It however differs from them as: (a) The application domains considered in E-MOTION are
linked to robotics and Bayesian modeling of cognitive systems, and (b) the learning aspect is just an element in the Bayesian
approach advocated by E-MOTION. Model building and inference are two important aspects of their approach which are dealt
with in the AMA research agenda. Despite these differences, the two teams do have common interests in terms of probabilistic
models and related problems: regularization, feature selection, ... Collaborations can thus be developed.
The GETALP team is interested in natural language processing (both written and spoken language), and makes use of probabilistic models, mainly for speech recognition and statistical machine translation. The training of such model in situations
where few annotated data is available is of primary importance for this team, which tries to solve the problem through acquisition of new data and active learning. Furthermore, GETALP is also interested in in learning abstract semantic representations,
for machine translation for example. This type of learning has traditionally been addressed with logical approaches (as ILP Inductive Logic Programming). Researchers now try to have a mixed approach, combining probabilities and logic, a domain
on which AMA could contribute.
The HADAS team conducts research on the deployment and running of data services in situations where both data and services
are distributed and dynamic (ubiquitous computing) and accessible (ambient computing). Part of its activity focuses on data
mining (through the works of A. Termier and M.-C. Rousset) and the discovery of (frequent) patterns in structured data, which
is strongly connected to part of our research agenda. We hope to be able to benefit from their expertise in several aspects of
the problem. Conversely, HADAS is also interested in using machine learning techniques (in particular case-based reasoning)
for the coordination of services (in particular the optimization of a request plan). Even if AMA does not intend to work on
case-based reasoning, we are interested in deploying other machine learning techniques in this context.
The METAH team conducts research on human learning, and makes use of models which radically differ from the ones used
in AMA. This being said, common interests exist between the two teams. On the one hand, METAH is interested in mining
and analyzing student logs derived during a certain learning task, a problem which involves both data analysis and machine
learning techniques. On the other hand, AMA is interested in social systems of learning individuals for which the models
developed in METAH could be relevant. We thus plan on further investigating these two points with members of the METAH
team.
The MAGMA team conducts research on multi-agent systems, and is particularly interested in understanding the notion
of ”emergence” in such systems. The research we want to develop on agent-based dynamic learning partly intersects the
MAGMA research agenda. As the focus of our research is on learning models within complex environments, we are likely
to be, at first, users of multi-agent systems. We however hope, in a near future, to foster stronger collaborations with the
MAGMA team on learning models in multi-agent systems.
The MRIM team conducts research on information retrieval models, for multilingual, multimedia data, with or without
structure. Several works in this team rely on machine learning techniques. The video annotation system developed in the
team, for example, relies on a cascade of SVM classifiers ( la stacking) and uses active learning to reduce the annotation
cost. Similarly, the problem of collaborative filtering studied in the team can be formulated as learning to rank problem
and addressed within this framework. More directly, the ANR project METRICC (see the contract section above), in which
both J.-P. Chevallet and E. Gaussier are involved, will allow a direct, formal collaboration between MRIM and AMA on
the probabilistic modeling of multilingual collection for information extraction. This collaboration can be extended to other
aspects, as learning to rank for information retrieval.
The PRIMA team is interested in using machine learning models (as reinforcement learning) for various aspects of its research. It is also interested in the integration of a temporal dimension in the analysis of the data it is dealing with. We thus
Page 11 on 22
hope to be able to collaborate with members of this team, definitely open to various aspects of machine learning and data
analysis.
Other teams in the LIG use machine learning techniques in their research. The AMA team is definitely interested in developing
collaborations in order to evaluate the models and methods developed in different application contexts, and to identify new,
important problems. We thus hope that fruitful collaborations and research will come out of the creation of the AMA team.
10.2
SWOT analysis
Strengths
Our main strength lies in the complementary expertise/experience of the individuals composing the team:
• Cécile Amblard, MCF, UJF, with expertise/experience in applied mathematics, and especially statistics (26th section,
CNU);
• Gilles Bisson, CR CNRS, with expertise/experoience in machine learning and artificial intelligence (7th section, CNRS);
• Ahlame Douzal, MCF UJF, with expertise/experience in data analysis and data mining (27th section, CNU);
• Catherine Garbay, DR CNRS, with expertise/experience in artificial intelligence and cognitive sciences (7th section,
CNRS);
• Eric Gaussier, PR UJF, with expertise/experience in machine learning and information access (27th section, CNU);
• Mirta Gordon, DR CNRS, with expertise/experience in statistical physics and cognitive science (7th section, CNRS).
and the fact that such expertise/experience is used to develop two aspects of machine learning: (a) inference of decision
functions and (b) computational models of the behavior of individuals and groups of individuals. The first aspect lies at the
intersection of artificial intelligence, computer science and applied mathematics, whereas the other one at the intersection
of dynamic systems, computer science and cognitive sciences. We bridge these two aspects through the study of complex
and structured data/systems evolving over time, which we believe makes our team unique in the French machine learning
landscape.
Furthermore, despite the complementary aspects or our expertise/experience, we already have collaborated on research
projects as well as on teaching activities. We thus believe our functioning as a single team should not raise big problems.
Weaknesses
Our main weakness lies the fact that, having different backgrounds, we also face some difficulties to understand each other.
Sharing a common vocabulary takes time, and that is something we have to develop in order to be able to work as a team
(we are currently working in that direction through weekly seminars and reading groups). Another weakness is related to the
fact that we are currently targeting publications in different domains. It will certainly be necessary to re-target some of our
publications towards machine learning in order to give a stronger positioning to the team.
Opportunities
As we said previously, we believe that the time is ripe now for integrating our various expertise (in applied mathematics,
artificial intelligence, machine learning, data analysis, cognitive sciences, information access and statistical physics) and
common interests into a single team within the LIG, the largest computer science lab in Grenoble. The main reasons for this
are:
1. Our research area is anchored in theoretical computer science (how to make machines learn from, reason on and adapt to
their environment) with applications in many different domains related to several LIG teams: Robotics, natural language
processing, information retrieval, computer vision, image processing;
2. Our research agenda directly addresses some of the major challenges on which the LIG is positioned:
• Large scale information access (conceptual challenge: Information Access)
• Modeling of complex, dynamic and structured data (conceptual challenge: Information Access)
• Analysis and modeling of social systems (societal challenge: Open Enterprise)
and fits well within the major themes put forward by the LIG:
• Sustainable and ambient systems: Our goal to develop models and algorithms to render systems adaptable to their
environment contributes to this theme;
Page 12 on 22
• Knowledge: Our goal to mine, analyze and model complex data represents an important step of the knowledge
extraction process;
• Green-by-IT: Environmental data presents interesting characteristics (sources are heterogeneous, the data is dynamic and partly structured) on which we could test our approaches and thus contribute to make better decisions
regarding the management of environment; our addressing this problem will mainly depend on the availability of
such data and the collaborations we can develop in this area.
3. Our team rallies and merges activities that were previously spread over different teams and labs, and offers the opportunity for a strong positioning around machine learning, data analysis and social systems.
Threats
No major threats identified.
11
Perspectives for the Research Team
As mentioned previously, machine learning has yielded, over the last years, different theoretical and practical tools, which can
be efficiently used in different contexts. These two aspects, theory and applications, will be present in our work, even though
we tend to position ourselves more strongly on the theoretical side than on the applied one (as can be shown from our current
publications, see next section, which rather target theoretical conference and journal venues in our domains).
Besides the permanent members of the team, we also plan on relying, to conduct our work, on PhD students and post-docs (9
PhD students are currently (co-)supervised by team members) as well as on existing collaborations and collaborations to be
set-up. Among the other institutes we are already working with, one can cite: CEA Grenoble, INRA (Grenoble and Jouy-enJosas), INRIA, LIP6, LJK, EPFL, ENS Paris, EHESS, Neuchâtel University, Orange Labs, Exalead, Xerox Research Center,
or Porto Alegre University.
Section 3 provided a detailed description of the research themes we want to develop in the AMA team. These research themes
are articulated around axes that will help us develop our research agenda, which we recall here while providing some of the
associated perspectives (see section 3 for more details):
1. Theme 1: Fundamentals of machine learning
• Axis: Proximity indices and metrics for complex data, aiming at studying and unifying different measures proposed for relational and temporal data.
• Axis: Phase transition in learning, aiming at developing new learning methods for Inductive Logic Programming
through the notion of partial coverage.
• Axis: Learning in high dimensions, aiming at developing parsimonious models for high dimensional data, through
regularization and pachinko-like approaches.
2. Theme 2: Analysis and exploration of complex data
• Axis: Classification, clustering and (co-)clustering, aiming at providing new methods for classification and clustering of complex, dynamic data.
• Axis: Visualization of relational data, aiming at providing better tools to visualize large dendrograms.
3. Theme 3: Learning and social networks
• Axis: Social systems of learning individuals, aiming at developing models which can account for all the interaction
types observed in groups of individuals.
• Axis: Agent based dynamic learning, aiming at articulating machine learning methods within a complex environment where solutions can only be built incrementally.
These themes are inter-related and aim at jointly developing two aspects of machine learning: One at the intersection of
artificial intelligence, computer science and applied mathematics, the other one at the intersection of dynamic systems, computer science and cognitive sciences. We bridge these two aspects through the study of complex and structured data/systems
evolving over time.
Page 13 on 22
12
Publications
International peer reviewed journal [ACL]
1. A.S. Silvent, Michel Dojat, Catherine Garbay. Multi-level temporal abstraction for medical scenario construction.
International Journal of Adaptive Control and Signal Processing, 19(5):377–394, 2005.
2. Amblard C., Girard S. (2005) Estimation procedures for a semiparametric family of bivariate copulas, Journal of Computational and Graphical Statistics, vol 14(2), pp 1-15.
3. H. Déjean, E. Gaussier, J.-M. Renders, F. Sadat. Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval. Artificial Intelligence in Medecine. Volume 33,
Issue 2. Feb., 2005.
4. Gordon M. B., Phan D., Nadal J-P., Vannimenus J. Seller’s dilemma due to social interactions between customers.
Physica A 356, Issues 2-4 (2005), pp. 628-640.
5. Nadal J-P., Phan D., Gordon M. B., Vannimenus J. Multiple equilibria in a monopoly market with heterogeneous agents
and externalities. Quantitative Finance vol 5, N6 (2005) 557-568.
6. Robinet V., Bisson G., Gordon M. B., Lemaire B. 2007. Induction of High-level Behaviors from Problem-solving
Traces using Machine Learning Tools. IEEE Intelligent Systems. 22(4), pp. 22-30.
7. Douzal-Chouakria, A., Nagabhushan, P.N. (2007). Adaptive dissimilarity index for measuring time series proximity.
Advances in Data Analysis and Classification Journal. 1, 5-21, Springer Berlin / Heidelberg.
8. Nathalie Richard, Michel Dojat, Catherine Garbay. Distributed Markovian segmentation: Application to MR brain
scans. Pattern Recognition, 40(12):3467-3480, 2007.
9. Florence Duchêne, Catherine Garbay, Vincent Rialle. Learning recurrent behaviors from heterogeneous multivariate
time-series. Artificial Intelligence in Medicine, 39(1):25-47, 2007.
10. Thomas Guyet, Catherine Garbay, Michel Dojat. Knowledge construction from time series data using a collaborative
exploration system. Journal of Biomedical Informatics, 40(6):672-687, 2007.
11. Prudent R., Moucadel V., Lopez-Ramos M., Aci S,. Laudet B., Mouawad L., Barette C., Einhorn J., Einhorn C., Denis
J-N., Bisson G., Schmidt F., Roy S,. Lafanechere L., Florent J-C., Cochet C. Expanding the chemical diversity of CK2
inhibitors. Molecular and Cellular Biochemistry Journal. 2008.
12. Semeshenko V., Gordon M B, Nadal J-P. Collective states in social systems with interacting learning agents. Physica A
387 (2008) 4903-4916.
13. Gordon M B, Nadal J-P, Phan D, Semeshenko V. Discrete choices under social influence: generic properties. Mathematical Models and Methods in Applied Sciences (M3AS) 19 (Supplementary Issue 1) (2009) 1441-1481.
14. Gordon M B, Iglesias J R, Nadal J-P, Semeshenko V. Crime and punishment: the economic burden of impunity. Eur.
Phys. J. B 68, 133-233 (2009).
15. Ma Y P, Gonçalves S, Mignot S,Nadal J.-P. and Gordon M.B. Cycles of cooperation and free-riding in social systems.
Eur. Phys. J. B 71, 597-610 (2009).
16. Semeshenko V, Garapin A, Ruffieux B, Gordon MB. Information-driven coordination: experimental results with heterogeneous individuals. Accepté pour publication dans Theory and Decision (2009)
17. Amblard C., Girard S. (2009), A new extention of bivariate FGM copulas, Metrika, vol 70, 1-17, 2009.
18. Douzal-Chouakria, A., Diallo, A., Giroud, F. (2009). Adaptive clustering for time series: application for identifying
cell cycle expressed genes. Computational Statistics and Data Analysis 53 (4), 1414-1426. Elsevier.
19. Benoit Scherrer, Florence Forbes, Catherine Garbay, Michel Dojat. Distributed Local MRF Models for Tissue and
Structure Brain Segmentation. IEEE transactions on medical imaging, 28(8):1296–1307, 2009.
20. Julie Bourbeillon, Catherine Garbay, Françoise Giroud. Mass data exploration in oncology: An information synthesis
approach. Journal of Biomedical Informatics, 42(6):612-623, 2009.
21. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. Agentification of Markov model-based segmentation: Application to magnetic resonance brain scans. Artificial Intelligence In Medicine, 46(1):81–95, 2009.
Page 14 on 22
National peer-reviewed journal [ACLN]
1. Nadal J-P., Gordon M. B. Physique statistique de phénomènes collectifs en sciences économiques et sociales. Mathématiques et Sciences Humaines (Mathematical Social Sciences) 172 (2005) 67-89.
2. Julie Bourbeillon, Catherine Garbay, Françoise Giroud. Une perspective analytique pour la recherche d’information.
Application : conception et evaluation de Tissue Microarrays. Revue ISI (Ingenierie des Systèmes d’Information),
1(11):109–135, 2006.
3. Leila Kefi, Catherine Berrut, Eric Gaussier. Un modèle de recherche d’informations fondé sur des critères d’obligation
et de certitude. Information Interaction Intelligence, 7(1):9–30, 2007. Note: Cépaduès - Edition.
4. Diallo, A., Douzal-Chouakria, A., Giroud, F. (2008). Classification adaptative de séries temporelles : application à
l identification des gènes exprimés au cours du cycle cellulaire. Revue des Nouvelles Technologies de l Information
(RNTI-E-11, ECG), 487-498, Cépaduès.
5. Stéphane Clinchant, Eric Gaussier. Modélisation probabiliste de collections textuelles et distributions de mots. Revue
des Nouvelles Technologies de l’Information - Apprentissage Automatique et Fouille de Données. Eds Y. Bennani et E.
Viennet, 2009.
Invited conferences, seminars and tutorials [INV]
1. E. Gaussier. Groupe de travail du GdR-PRC I3, Indexation et Recherche d’Informations, 8 juillet 2005. Apprentissage
statistique pour l’indexation et la recherche d’information multilingue.
2. E. Gaussier. Conférence sur la Recherche d’Information et ses Applications, CORIA 2005. Apprentissage et Recherche
d’Information.
3. E. Gaussier. Seminar of the Computer Science Department, Cornell University, USA, 2005; Machine Learning for
Textual Inforamtion Access
4. Amblard C., Girard S. (2006) A semiparametric family of bivariate copulas: dependence properties and estimation
procedures, 69th Annual Metting of Institute of Mathematical Statistics, Rio de Janeiro, Brésil.
5. E. Gaussier. Ecole d’Automne en Recherche d’Information et ses Applications (EARIA), oct. 2006 ; Quelle langue
pour la RI ? (cours partagé avec J. Savoy, de l’Université de Neuchâtel)
6. Bisson G. Distances entre données et application aux cartes de Kohonen. Rencontres RIA 2006 (Rencontre InterAssociations). 20-21 mars 2006, Lyon.
7. Gordon M. B. Interaction entre capacité d’apprentissage et évolution. Journées 2006 Génétique et Cognition du Rescif
(Réseau de Sciences Cognitives Ile de France), Paris, 12-13 mai 2006
8. E. Gaussier. Recherche d’Information : fondements et modèles. Congrès INFORSID, Perros Guirec, France, 2007.
9. E. Gaussier. Seminar of the Computer Science Department, Glasgow University, UK, 2007; Bustiness and IR models
10. Gordon M. B. Emergent collective states in social systems with heterogeneous interacting agents. MASHS2007, Mai
10-11 2007, Brest, France.
11. Gordon M. B. Emergent collective states in social systems with heterogeneous interacting agents. NICO Conference:
Dynamics and Complexity in People and Societies, Northwestern University, Illinois, 22-24 October 2007
12. Bisson G. Fouille de données éducatives. 5ème école thématique du CNRS sur les EIAH. 7-12 juillet 2007, Saint
Quentin sur Isère.
13. Catherine Garbay. Cognition & Communication Homme-Machine : De nouveaux défis pour les sciences de la complexité. Journée Cognition : entre sciences et technologies organisée à l’occasion de la remise à Hélène Lovenbruck
de la médaille de bronze du CNRS, Grenoble, France, may 2007.
14. E. Gaussier. Annotation and Ontologies in the Context of Information Retrieval. Information Retrieval Facility Symposium, Vienna, Austria, 2008.
Page 15 on 22
15. E. Gaussier. Modélisation probabiliste de collections textuelles. Troisième édition du colloque Apprentissage Artificiel
et Fouille de Données, Paris, France, 2008.
16. Bisson G. Clustering of Molecules and structured data. The 32th Conférence GfKL 2008 (German Classication Society),
16-18 juillet, Hambourg.
17. Gordon M. B. Crime and punishment: the economic burden of impunity. Mathematical Models for Criminality. Pisa,
April 17-18, 2008.
18. Gordon M. B. Cycles in cooperation and free-riding. The 5th European Conference on Complex Systems, Jerusalem,
September 14-19, 2008
19. Catherine Garbay. Exposé de synthèse. Journée satellite Enaction : une voie possible pour un dialogue entre sciences
informatiques et sciences cognitives à RFIA’08, Amiens, France, jan 2008.
20. Catherine Garbay, D. Kayser, J. Lorenceau, S. Ploux. Intelligence Artificielle et Sciences Cognitives (exposé de
synthèse). Journées d’Intelligence Artificielle Fondamentale 2008 (IAF’08), Paris, France, nov 2008.
21. E. Gaussier. Séminaire INRIA/LEAR, jan. 2009, INRIA Rhône-Alpes; Probabilistic Models of Text Collections for
Information Access.
22. E. Gaussier. Seminar of the Computer Science Lab of NCRS Demokritos, Athens, 2009; Probabilistic Models for
Textual Information Retrieval
23. Gordon M. B. Cycles in social systems without reciprocity. ANR-NSF Workshop on Dynamics in the Human Sciences:
Cognitive, Behavioral & Social Complexity. 27 et 28 Avril 2009 REIMS (France)
24. Gordon M. B. Models of social systems : multiple equilibria and cycles. International Symposium on Complex Systems
Science. Paris, September 17-18, 2009
25. Catherine Garbay. Computer Vision: A Plea for a Constructivist View. Proc. of the 12th Conference on Artificial
Intelligence in Medicine Europe (AIME’09), Lecture Notes in Artificial Intelligence, (C. Combi, Y. Shahar & A. AbuHanna eds), 2009.
International peer-reviewed conferences with proceedings [ACTI]
1. Thomas Guyet, Catherine Garbay, Michel Dojat. Silvia Miksch. Human-Computer interaction to learn scenarios from
ICU multivariate time series. Proc of the 10th Conference on Artificial Intelligence in Medicine Europe (AIME’05),
Lecture Notes in Artificial Intelligence, (S. Miksch, J. Hunter & E. Keravnou, eds), 2005.
2. Julie Bourbeillon, Catherine Garbay, Jolle Simony-Lafontaine, Françoise Giroud. Multimedia Data Management to
Assist Tissue Microarrays Design. Proc of the 10th Conference on Artificial Intelligence In Medicine, AIME, Lecture
Notes in Artificial Intelligence, (S. Miksch, J. Hunter & E. Keravnou, eds), 2005.
3. E. Gaussier, C. Goutte. Learning with partially labelled data - with confidence. International Workshop on Learning
with Partially Classified Data, International Conference on Machine Learning, 2005.
4. M. Simard, N. Cancedda, B. Cavestro, M. Dymetman, E. Gaussier, C. Goutte, K. Yamada, P. Langlais, A. Mauser.
Translating with non-contiguous phrases. Joint HLT/EMNLP conference, 2005.
5. C. Goutte, E. Gaussier. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation.
Proceedings of the 27th European Conference on Inforamtion Retrieval 2005 (ECIR-05).
6. Gordon M. B., Phan D., Waldeck R., Nadal J-P. Cooperation and free-riding with moral cost. In Proceedings of International Conference on Cognitive Economics, Sofia-Bulgaria, August 5-8 2005. In Advances in Cognitive Economics,
NBU Series in Cognitive Sciences, 2005 B. Kokinov ed.
7. Pradeep, K., P.N. Nagabhushan, Douzal-Chouakria, A. (2006). WaveSim and Adaptive Transform for subsequence
Time series Clustering. IEEE 9th International Conference on Information Technology (ICIT’06), 197-202.
8. J.-M. Renders, E. Gaussier, C. Goutte, F. Pacull, G. Csurka. Categorization in multiple category systems. International
Conference on Machine Learning, International Conference on Machine Learning, 2006.
Page 16 on 22
9. M. Nyffenegger, J.-C. Chappelier, E. Gaussier. Revisiting Fisher Kernels for Document Similarities. European Conference on Machine Learning, European Conference on Machine Learning, 2006.
10. S. Clinchant, C. Goutte, E. Gaussier. Lexical Entailment for Information Retrieval. European Conference on Information Retrieval, European Conference on Information Retrieval, 2006.
11. Aci S., Bisson G., GordonM. B., Roy S., Wieczorek S. Partial Subsumption Test and Phase Transition. Proceedings of
ILP conference. Santiago 24-27 august 2006, Spain, pp 40-44.
12. Wieczorek S., Bisson G, Gordon M. B. Guiding the Search in the NO Region of the Phase Transition Problem with a
Partial Subsumption Test. European Conference on Machine Learning (ECML 2006) Berlin, September 18-22 2006, in
Machine Learning: ECML 2006. Lecture Notes in Computer Science (2006) 817-824
13. Thomas Guyet, Catherine Garbay, Michel Dojat. Benoit Bardy, Denis Mottet. Computer/Human Structural Coupling
for Data Interpretation. Proc ENACTIVE 2006: Third International Conference on Enactive Interfaces, Enaction and
Complexity, Montpellier, France, nov 2006.
14. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. Distributed and Cooperative Markovian Segmentation of tissues and structures in MRI brain scans. Human Brain Mapping Meeting, HBM 2006, June, 2006, Florence,
Italie, 2006.
15. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. Une Approche SMA pour la Segmentation Markovienne des Tissus et Structures Présents dans les IRM Cérébrales. 2èmes journées algéro-françaises en imagerie médicale,
JETIM 2006, November, 2006, Alger, Algérie, 2006.
16. Douzal-Chouakria, A., A. Diallo, F. Giroud (2007). Adaptive clustering for time series: application for identifying cell
cycle expressed genes. International Association for Statistical Computing, Statistics for Data Mining, Learning and
Knowledge Extraction (IASC 07), Aveiro, Portugal.
17. M. Barbaiani, N. Cancedda, C. Dance, S. Fazekas, T. Gaal, Eric Gaussier. Asymmetric Term Alignment with Selective Contiguity Constraints by Multi-Tape Automata. Finite State Methods in Natural Language Processing, Postdam,
Germany, 2007.
18. Marchal, H., Bianco, M., Dessus, P., Lemaire, B. The Development of Lexical Knowledge: Toward a Model of the
Acquisition of Lexical Gender in French. In S. Vosniadou, D. Kayser (Eds), Proceedings of the Second European
Conference on Cognitive Science. 2007.
19. Robinet V., Bisson G., Gordon M., Lemaire B. Searching for student intermediate mental steps. In Proceedings of the
Workshop on Data Mining for User Modeling. 11th International Conference on User Modeling, Corfu, Greece, June
25, 2007.
20. Semeshenko V., Garapin A., Ruffieux B., Gordon M. B. Coordination and self-organization in social systems: experiments and learning models, in Multi-Agents for modeling Complex Systems (MA4CS), Dresden, Germany, October
4th, 2007.
21. Semeshenko V., Garapin A., Ruffieux B., Gordon M. B. Self-organization of social systems: empirical results and
learning models. Conférence ARCo’07, Nancy, 28-30 nov 2007, in Acta-Cognitica (2007) 103-114
22. Thomas Guyet, Catherine Garbay, Michel Dojat. A Human-Machine Cooperative Approach for Time Series Data
Interpretation. Proceedings of the 11th Conference on Artificial Intelligence In Medicine, AIME’07, Lecture Notes in
Articial Intelligence, (Riccardo Bellazzi, Ameen Abu-Hanna Jim Hunter eds), Amsterdam, Neerderland, jul 2007.
23. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. LOCUS: LOcal Cooperative Unified Segmentation
of MRI Brain Scans. 11th International Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI), Berlin, Germany, 2007.
24. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. MRF Agent Based Segmentation: Application to
MRI Brain Scans. Proc. of the 11th Conference on Artificial Intelligence in Medicine Europe (AIME’07), Lecture
Notes in Artificial Intelligence, (Riccardo Bellazzi, Ameen Abu-Hanna & Jim Hunter eds), 2007.
25. Y. Kabir, Michel Dojat, Benoit Scherrer, Catherine Garbay, Florence Forbes. Multimodal MRI segmentation of ischemic
stroke lesions. Proc of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society
(EMBS 2007), 2007.
Page 17 on 22
26. Stéphane Clinchant, Eric Gaussier. The Beta-Negative Binomial for Text Modeling. 30th European Conference on
Information Retrieval (ECIR 2008), Glasgow, 2008.
27. Ali Mustafa Qamar, Eric Gaussier, Jean-Pierre Chevallet, Joo-Hwee Lim. Similarity Learning for Nearest Neighbor
Classification. 8th IEEE International Conference on Data Mining (ICDM) 2008, Pisa, Italy, dec 2008.
28. Loic Maisonnasse, Eric Gaussier, Jean-Pierre Chevallet. Multiplying Concept Sources for Graph Modeling. Advances
in Multilingual and Multimodal Information Retrieval, 2008. Note: C. Peters, V. Jijkoun, T. Mandl, H. Muller, D.W.
Oard, A. Peas, V. Petras, D. Santos, (Eds.). Selected Proceedings of CLEF 2007.
29. Loic Maisonnasse, Philippe Mulhem, Eric Gaussier, Jean-Pierre Chevallet. LIG at ImageCLEF 2008, Evaluating Systems for Multilingual and Multimodal Information Access. Proc. of the 9th Workshop of the Cross-Language Evaluation Forum, LNCS, 2008.
30. Bisson G. Hussain F. χ-Sim: A new Similarity Measure for the Co-clustering Task. Proceedings of the 7th International
conference on Machine Learning and Application (ICMLA). 11-13 décembre, San Diego, United States. 2008.
31. Bisson G., Hussain F. A Co-Similarity Measure. Proceedings of the first joint conference of SFC and CLADAG. 11-13
june, Caserta, Italy. pp 197-200. 2008.
32. Loic Maisonnasse, Eric Gaussier, Jean-Pierre Chevallet. Model Fusion in Conceptual Language Modeling. 31st European Conference on Information Retrieval (ECIR 09), Toulouse (France), 2009.
33. Trong-Ton Pham, Loic Maisonnasse, Philippe Mulhem, Eric Gaussier. Visual Language Model for Scene Recognition.
Singaporean-French IPAL Symposium, SinFra 2009, 2009.
34. Stéphane Clinchant, Eric Gaussier. Bridging Language Modeling and Divergence from Randomness Approaches: a
Log-Logistic Model for IR. 2nd International Conference on the Theory of Information Retrieval, Cambridge, UK,
2009.
35. Stéphane Clinchant, Eric Gaussier. A Log-Logistic Model for Information Retrieval. 18th ACM Conference on Information and Knowledge Management, Hong-Kong, China, 2009.
36. Ali Mustafa Qamar, Eric Gaussier. Online and Batch Learning of Generalized Cosine Similarities. IEEE International
Conference on Data Mining, Florida, USA, 2009.
37. A Diallo, A Douzal-Chouakria, Franoise Giroud (2009). Which Distance for the Identification and the Differentiation
of cell-cycle Expressed Genes?. 8th International Symposium on Intelligent Data Analysis. 273-284.Springer-Verlag
(2009).
National peer-reviewed conferences with proceedings [ACTN]
1. Thomas Guyet, Catherine Garbay, Michel Dojat. Apprentissage de scénarios à partir de séries temporelles multivariées.
Journées francophones d’Extraction et de Gestion des Connaissances (EGC’2005), Paris, France, jan 2005.
2. Florence Duchêne, Catherine Garbay, Vincent Rialle. Apprentissage non supervisé de motifs temporels, multidimensionnels et hétérogènes. Application à la télésurveillance médicale. Conférence francophone sur l’apprentissage automatique (Cap05), Nice, France, 2005.
3. Julie Bourbeillon, Catherine Garbay, Françoise Giroud. Génération de documents multimedia adaptatifs dans une
perspective analytique. INFORSID, Grenoble, may 2005.
4. Thomas Guyet, Catherine Garbay, Michel Dojat. Partenariat utilisateur-SMA pour l’apprentissage à partir de séries
temporelles. Journées Françaises de Systèmes Multi-Agents (JFSMA), Calais, France, nov 2005.
5. M. Simard, N. Cancedda, B.Cavestro, M. Dymetman, E. Gaussier, C. Goutte, P. Langlais, A. Mauser, K. Yamada.
Traduction automatique statistique avec des segments discontinus. Conférence sur le Traitement Automatique des
Langues Naturelles, Dourdan, 2005.
6. Leila Kefi, Catherine Berrut, Eric Gaussier. Indexation complexe de documents : vers une vérification qualitative.
Congrès INFORSID, Grenoble, may 2005.
Page 18 on 22
7. Michelland S., Godart F., Amblard C., de Fraipont F., Moro-Sibilot D., Garin J., Seve M., Favrot MC, (2006) Mises
en évidence de marqueurs du cancer du poumon non à petites cellules dans le plasma par la technique de SELDITOF (Surface Enhanced Laser Desorption Ionization - Time Of Flight), premières journées scientifique du CLARA,
Vulcania, Clermont-Ferrand, France et acte dans Bulletin du Cancer 2006, 93-99.
8. Leila Kefi, Catherine Berrut, Eric Gaussier. Un modèle de RI basé sur des critères d’obligation et de certitude.
Conférence en Recherche d’Information et Applications CORIA’06, Lyon France, mar 2006.
9. Robinet V., Bisson G., Lemaire B., Gordon M.B. Modélisation de profils d’élèves à partir de traces de résolution de
problèmes. Actes de la 8eme conférence francophone sur l’apprentissage automatique-CAp 2006 (Trégastel, 22-24 mai
2006) 401-402.
10. Wieczorek S., Bisson G., Roy S., Gordon M.B. Etude du comportement d’un indice de subsomption sur des données
relationnelles. Actes de la 8eme conférence francophone sur l’apprentissage automatique-CAp 2006 (Trégastel, 22-24
mai 2006) 399-400.
11. Benoit Scherrer, Michel Dojat, Florence Forbes, Catherine Garbay. Segmentation Markovienne distribuée et coopérative
des tissus et des structures présents dans des IRM cérébrales. 15ème Congres Reconnaissance des Formes et Intelligence
Artificielle, RFIA 2006, Tours, France, 2006.
12. Bisson G., Wieczorek S,. Aci S., Roy S.. classification automatique de molécules. Actes de la 9eme conférence
francophone sur l’apprentissage automatique-CAp 2007 (Grenoble, 4-6 juillet 2007).
13. Loic Maisonnasse, Eric Gaussier, Jean-Pierre Chevallet. Modélisation de relations dans l’approche modèle de langue
en recherche d’information. CORIA 2008, Perros Guirec, France, 2008.
14. Bisson G., Wieczorek S., Aci S., Roy S. Impact du choix de la distance sur la classification d’un ensemble de molécules.
Actes de l atelier Mesures de Similarité Sémantique de la conférence EGC. 29 janvier 2008, Sophia-Antipolis, pp 109119.
15. Amblard C, S. Michelland, F. de Fraipont, D. Moro-Sibilot, F. Godard, MC Favrot, M. Seve (2009) Recherche d’une
signature protéomique du cancer du poumon, Journées de Statistiques, Bordeaux.
16. Ali Mustafa Qamar, Eric Gaussier. Apprentissage de différentes classes de similarité dans les k-PPV. XVIièmes Rencontres de la Société Française de Classification, Grenoble, France, 2009.
17. Trong-Ton Pham, Loic Maisonnasse, Philippe Mulhem, Eric Gaussier. Modèle de langue visuel pour la reconnaissance
de scènes. Actes de la conférence CORIA, Giens, France, 2009.
Oral communications, without proceedings, in international or national events (e.g. tutorials,
courses in summer schools,. . . ) [COM]
1. Chouakria-Douzal, A. (2005). On the distance measure between time series. International Association for Statistical
Computing (IASC 05), Chypres.
2. Wieczorek S, Barette C, Lafanechère L, Gordon M-B, Maréchal E, Horvath D, Bisson G, Roy S. Projet Accamba :
comment décrire de façon pertinente des molécules pour expliquer et prédire leur bio-activité par apprentissage artificiel? . Poster au congrès de la SFBBM (Société Française de Biochimie et Biologie Moléculaire). Nantes les 24-26
octobre 2005.
3. Phan D., Waldeck R., Gordon M.B., Nadal J-P. Adoption and cooperation in communities: mixed equilibrium in polymorphic populations. Oral presentation at Wehia05 (University of Essex, United Kingdom, June 13-15, 2005).
4. Douzal-Chouakria, A. (2006). Réduction de la dimension de séries temporelles multidimensionnelles par extraction des
tendances locales. Extraction et Gestion des Connaissances (EGC’06), Atelier Fouille de données temporelles, Lille.
5. Douzal-Chouakria, A., Diallo, A., Giroud, F. (2006). Adaptive dissimilarity index for genes expression profiles classification. Integrative Post Genomics Conference. Lyon.
6. Aci S., Wieczorek S , Barette C, Lafanechère L, Gordon MB, Maréchal E, Horvath D, Bisson G, Roy S. 2006. The
ACCAMBA Project: finding the proper way to describe molecules in order to explain and predict their bioactivity using
machine learning methods. Workshop Chemoinformatics in Europe: Research and Teaching, Obernai, France, 29 May
- 1st June, 2006.
Page 19 on 22
7. Aci S, Charavay S, Wieczorek S, Lafanechère L, Maréchal E, Horvath D, Bisson G, Roy S. 2006. Using ChemAxon
tools in Accamba, a project for modeling screening results based on machine learning methods. ChemAxon 2006 User
Group Meeting Budapest, Hungary 7-8 June 2006.
8. Garapin A., Ruffieux B., Gordon M.B., Semeshenko V. Designing efficient coordination in games with inefficient
equilibria. Présentation orale, 8ièmes Journées d’Économie Expérimentale, 1-2 juin 2006, BETA, Strasbourg.
9. Garapin A., Ruffieux B., Gordon M.B., Semeshenko V. From dying to rising seminars: Designing efficient coordination in the Schelling critical mass game. Présentation orale à IAREP-SABE Meeting on Behavioural Economics and
Economic Psychology, July 5-6 2006, Paris.
10. Gordon M.B., Nadal J-P., Phan D., Semeshenko V.. Discrete choices under social influence: General properties. 1st International conference on Economic Sciences with Heterogeneous Interacting Agents (ESHIA)-WEHIA 2006, Bologna,
Italy, 15-17 June 2006.
11. Ma Y., Gordon M. B., Nadal J-P. Dynamics of social systems : cooperation and free-riding. Poster presentation. Physics
of Socio-Economic Systems - AKSOE (Dresden, 27-30 mars 2006).
12. Semeshenko V., Gordon M. B., Nadal J-P. Learning through social interactions. Oral presentation. Physics of SocioEconomic Systems - AKSOE (Dresden, 27-30 mars 2006).
13. Douzal-Chouakria, A., N. Hammami, C. Garbay (2007). Local Factorial Analysis of Time Series. 56th Session of the
International Statistical Institute (ISI 07), Lisboa, Portugal.
14. Rizk, G., Douzal-Chouakria, A., Amblard C.(2007). Temporal Decision Trees. 56th Session of the International Statistical Institute (ISI 07), Lisboa, Portugal.
15. Giroud, F., Diallo, A., Douzal-Chouakria, A. (2007). Identification of cell cycle expressed genes. Workshop Towards
Systems Biology, Grenoble.
16. Giroud, F., Diallo, A., Douzal-Chouakria, A. (2007). A new approach for molecular dynamic network analysis.
Réaumur Meeting, Grenoble.
17. Giroud, F., Diallo, A., Douzal-Chouakria, A. (2007). Identification of cell cycle expressed genes: a new approach for
molecular dynamic network analysis. Workshop Towards Systems Biology 2007, Grenoble, France.
18. N. Cancedda, Marc Dymetman, Eric Gaussier, C. Goutte. An Elastic-Phrase Model for Statistical Machine Translation.
Journées de l’ATALA (Assocation pour le Traitement Automatique des Langues), Paris, 2007.
19. Bisson G., Aci S., Sylvaine Roy, Wieczorek S. (2007). Classification automatique de molécules. Actes de la Conférence
d Apprentissage Cap 2007. Grenoble 4-6 juillet 2007.
20. Ali Mustafa Qamar, Eric Gaussier. Similarity Learning in Nearest Neighbor and Application to Information Retrieval.
Symposium on Future Directions in Information Access, Padua, Italy, 2009.
Posters in international or national conferences and workshops [AFF]
1. E. Gaussier, C. Goutte. Relation between PLSA and NMF and Implications. 28th Annual International ACM SIGIR
Conference, Poster Session, 2005.
2. Loic Maisonnasse, Eric Gaussier, Jean-Pierre Chevallet. Revisiting the dependence language model for information retrieval. SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development
in information retrieval. Poster session., New York, NY, USA, 2007.
Scientific books and book chapters [OS]
1. Chouakria-Douzal, A., and P.N. Nagabhushan (2006). Improved Fréchet Distance for Time Series. In: V. Batagelj,
H.-H. Bock, A. Ferligoj, A. Ziberna (eds.) Data Science and Classification, 13-20, Springer, ISBN: 3-540-34415-2.
2005.
2. Dreyfus, J-M. Martinez, M. Samuelides, M. B. Gordon, F. Badran, S. Thiria, L. Hérault. Neural Networks, Methodology
and Applications. Collection : Artificial Intelligence (2005), ISBN 3-540-22980-9, Springer.
Page 20 on 22
3. Viktoriya Semeshenko, Mirta B. Gordon, Jean-Pierre Nadal, Denis Phan. Choice under social influence: effects of
learning behaviors on the collective dynamics. Book Chapter 8, in Contributions to Economic Analysis, volume 280,
Cognitive Economics: New Trends edited by R. Topol and B. Walliser, Elsevier (2006)
4. Douzal-Chouakria, A., Diallo, A., F. Giroud (2007). Adaptive dissimilarity index for Gene Expression Profiles Classification. In: Selected Contributions in Data Analysis and Classification, Series: Studies in Classification, Data Analysis,
and Knowledge Organization, Brito, P., Bertrand, P., Cucumel, G., De Carvalho, F. (Eds.). XIII, 483-494, Springer
Berlin Heidelberg. 2006.
5. H. Déjean, E. Gaussier. Une nouvelle approche à l’extraction de lexiques bilingues à partir de corpus comparables.
Corpus Linguistics: Critical Concepts in Linguistics, edited by W. Teubert and R. Krishnamurthy, Routledge Publishers,
June 2007 (compilation in 6 volumes of papers on corpus linguistics).
6. Aci S., Bisson G., Sylvaine Roy, Wieczorek S. Clustering of Molecules: Influence of the Similarity Measures. In
Selected Contributions in Data Analysis and Classification. Springer. Brito, P., Bertrand, P., Cucumel, G., De Carvalho,
F. (Eds). Page 433-445, 2007.
7. Bisson G. Chapitre 14: Apprentissage artificiel et données de criblage . Chémogénomique. Des petites molécules
pour explorer le vivant, Une introduction à l’usage des biologistes, chimistes et informaticiens. Maréchal E., Roy S.,
Lafanechère L., Cros J. (éditeurs). (2007). EDP SCIENCES. 2007.
8. E. Gaussier, F. Pacull. Distributed Categorizer for Large Category System. Mining Massive Datasets for Security Selected Proceedings of the MMDSS 2007 NATO workshop, IOS Press, 2008.
9. Dreyfus G., Martinez J-M., Samuelides M., Gordon M. B., Badran F., Thiria S., Hérault L. Apprentissage statistique
(3ème édition de: Réseaux de neurones - Méthodologie et applications). Collection Algorithmes, Eyrolles (2002, 2ème
édition 2004) Livre de 386 pages, ISBN 2-212-11019-7. 3ème édition : Chapitre 6 (auteur: M. B. Gordon) augmenté
avec Support Vector Machines. 2008.
10. Nadal J-P., Gordon M. B. Choix sous influence sociale : heurs et malheurs de la coordination. Systèmes Complexes en
SHS, Eds. Walliser et Orléan (2008).
11. Viktoriya Semeshenko. Interacting Learning Agents: Models, Simulations, Experiments. PhD Thesis published as a
book. VDM Verlag Publisher. 2009.
Scientific popularization [OV]
None.
Book or Proceedings editing [DO]
None.
Doctoral Dissertations and Habilitations Thesis [TH]
1. Renaudie David : ”Application de techniques d’apprentissage machine à la modélisation d’élèves en interaction avec
un environnement informatique d’apprentissage humain : apprentissage de l’algèbre avec Aplusix”. Co-encadrement
Mirta Gordon (50%), Gilles Bisson (50%). Thèse INPG, soutenue le 14/01/05.
2. Kefi Leila : ”Un modèle général de recherche dinformation : Application à la recherche de documents techniques par
des professionnels”. Co-encadrement C. Berrut (50%), E. Gaussier (50%). Thèse UJF, soutenue le 10/10/06.
3. Semeshenko Viktoriya : ”Modèles de choix discrets de systèmes économiques et sociaux : approches par la physique
statistique.” Directeur de thèse : Mirta Gordon. Thèse INPG, soutenue le 25/06/2007.
4. Bourbeillon J. ”Vers une synthèse d’information orientée tâche - Application à la conception et à l’évaluation de Tissue
MicroArrays”. Co-encadrement C. Garbay (50%), F. Giroud (50%). Thèse UJF, soutenue en octobre 2007.
5. Guyet T. ”Interprétation collaborative de séries temporelles. Application à des données de réanimation médicale”.
Co-encadrement C. Garbay. PhD thesis, INPG. November 2007.
Page 21 on 22
6. Scherrer B. ”Segmentation des tissus et structures sur les IRM crbrales : agents markoviens locaux coopratifs et formulation baysienne”. Co-encadrement C. Garbay (50%), M. Dojat (50%). Thèse INPG, soutenue le 12/12/2008.
7. Wieczorek Samuel : ”Analyse de Chimiothèques et Construction Automatique de Modèles de Bio-Activité.” Coencadrement Gilles Bisson (80%), Mirta Gordon (20%). Thèse UJF, spécialité Informatique, soutenue le 7/7/2009.
8. Robinet Vivien : ”Modélisation de profil cognitif d’élèves à partir de traces de résolution de problèmes.” Co-encadrement
Benot Lemaire (80%), Mirta Gordon (20%). Thèse EDICSE, INPG, spécialité sciences cognitives, soutenance le
7/12/2009.
Summary
The following table summarizes the different publications per year.
ACL -International peer reviewed journal
ACLN National peer-reviewed journal
INV - Invited conferences, seminars and tutorials
ACTI - International peer-reviewed conference proceedings
ACTN - National peer-reviewed conferences with proceedings
COM - Oral communications, without proceedings, in international or
national events (e.g. tutorials, courses in summer schools,. . . )
AFF- Posters in international or national conferences and workshops
OS – Scientific books and book chapters
OV –Scientific popularization
DO – Book or Proceedings editing
TH –Doctoral Dissertations and Habilitations Theses
Total
2005
5
1
3
6
6
2006
1
4
9
5
2007
5
1
6
13
1
2008
2
1
7
6
2
2009
9
1
5
6
3
Total
21
5
25
39
17
3
9
7
-
1
20
1
2
1
28
1
1
30
1
4
3
41
3
1
22
1
2
28
2
11
8
149
Page 22 on 22

Team : AMA

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