Project-Team:MODAL

Project-Team Modal

Team, Visitors, External Collaborators

Overall Objectives

Context
Goals

Research Program

Research axis 1: Unsupervised learning
Research axis 2: Performance assessment
Research axis 3: Functional data
Research axis 4: Applications motivating research

Application Domains

Economic World
Biology

Highlights of the Year

New Software and Platforms

MixtComp
BlockCluster
CloHe
PACBayesianNMF
pycobra
STK++
rtkore
MixAll
simerge
Platforms

New Results

Axis 1: Data Units Selection in Statistics
Axis 1: Model-Based Co-clustering for Ordinal Data
Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions
Axis 1: Model-based co-clustering for mixed type data
Axis 1: Model-Based Co-clustering with Co-variables
Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data
Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
Axis 1: A targeted multi-partitions clustering
Axis 1: Co-clustering: A versatile way to perform clustering in high dimension
Axis 1: Dealing with missing data in model-based clustering through a MNAR model
Axis 1: Self Organizing Coclustering for textual data synthesis
Axis 1: Linking canonical and spectral clustering
Axis 1: Multiple partition clustering
Axis 2: Change-point detection by means of reproducing kernels
Axis 2: New efficient algorithms for multiple change-point detection with kernels
Axis 2: Multi-Layer Group-Lasso
Axis 2: Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
Axis 2: A Quasi-Bayesian Perspective to Online Clustering
Axis 2: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Axis 2: Simpler PAC-Bayesian bounds for hostile data
Axis 2: PAC-Bayesian high dimensional bipartite ranking
Axis 2: Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Axis 3: Clustering spatial functional data
Axis 3: Categorical functional data analysis
Axis 4: Real-time Audio Sources Classification
Axis 4: Matching of descriptors evolving over time
Axis 4: Supervised multivariate discretization and levels merging for logistic regression
Axis 4: MASSICCC Platform for SaaS Software Availability
Axis 4: ClinMine: Optimizing the Management of Patients in Hospital
Projection Under Pairwise Control

Bilateral Contracts and Grants with Industry

Bilateral Contracts: SEMENCES DE FRANCE
Bilateral Contracts: Arcelor-Mittal
Bilateral Contracts: Alstom
Bilateral Contracts: Decathlon

Partnerships and Cooperations

Regional Initiatives
National Initiatives
European Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Major publications
Publications of the year

Inria | Raweb 2018 | Presentation of the Project-Team MODAL


	PDF	e-Pub

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Section: Bilateral Contracts and Grants with Industry

Bilateral Contracts: Alstom

Participants : Christophe Biernacki, Benjamin Guedj.

Alstom is a world leader company in integrated transport systems. This contract aims at optimizing predictive maintenance from free text annotations provided by maintenance people. The proposal consists in using co-clustering as a way for grouping both maintenance operations and words describing them.

It is a joint work with Etienne Goffinet (InriaTech engineer).

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