Bibliography

Major publications by the team in recent years

• 1P. Alquier, B. Guedj.
  
  Simpler PAC-Bayesian Bounds for Hostile Data, in: Machine Learning, 2018. [ DOI : 10.1007/s10994-017-5690-0 ]
  
  https://hal.inria.fr/hal-01385064
• 2P. Bathia, S. Iovleff, G. Govaert.
  
  An R Package and C++ library for Latent block models: Theory, usage and applications, in: Journal of Statistical Software, 2016.
  
  https://hal.archives-ouvertes.fr/hal-01285610
• 3C. Biernacki, J. Jacques.
  
  Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm, in: Statistics and Computing, 2016, vol. 26, n^o 5, pp. 929-943.
  
  https://hal.inria.fr/hal-01052447
• 4C. Biernacki, A. Lourme.
  
  Unifying Data Units and Models in (Co-)Clustering, in: Advances in Data Analysis and Classification, May 2018, vol. 12, n^o 41.
  
  https://hal.archives-ouvertes.fr/hal-01653881
• 5A. Celisse.
  
  Optimal cross-validation in density estimation with the L2-loss, in: The Annals of Statistics, 2014, vol. 42, n^o 5, pp. 1879–1910.
  
  https://hal.archives-ouvertes.fr/hal-00337058
• 6S. Dabo-Niang, C. Ternynck, A.-F. Yao.
  
  Nonparametric prediction in the multivariate spatial context, in: Journal of Nonparametric Statistics, 2016, vol. 28, n^o 2, pp. 428-458. [ DOI : 10.1080/10485252.2016.01.007 ]
  
  https://hal.inria.fr/hal-01425932
• 7J. Dubois, V. Dubois, H. Dehondt, P. Mazrooei, C. Mazuy, A. A. Sérandour, C. Gheeraert, P. Guillaume, E. Baugé, B. Derudas, N. Hennuyer, R. Paumelle, G. Marot, J. S. Carroll, M. Lupien, B. Staels, P. Lefebvre, J. Eeckhoute.
  
  The logic of transcriptional regulator recruitment architecture at cis -regulatory modules controlling liver functions, in: Genome Research, June 2017, vol. 27, n^o 6, pp. 985 - 996. [ DOI : 10.1101/gr.217075.116 ]
  
  https://hal.archives-ouvertes.fr/hal-01647846
• 8M. Marbac, C. Biernacki, V. Vandewalle.
  
  Model-based clustering of Gaussian copulas for mixed data, in: Communications in Statistics - Theory and Methods, December 2016.
  
  https://hal.archives-ouvertes.fr/hal-00987760
• 9M. Marbac, V. Vandewalle.
  
  A tractable Multi-Partitions Clustering, in: Computational Statististics & Data Analysis, July 2018. [ DOI : 10.1016/j.csda.2018.06.013 ]
  
  https://hal.inria.fr/hal-01691417
• 10C. Preda, A. Dermoune.
  
  Parametrizations, fixed and random effects, in: Journal of Multivariate Analysis, February 2017, vol. 154, pp. 162 - 176. [ DOI : 10.1016/j.jmva.2016.11.001 ]
  
  https://hal.archives-ouvertes.fr/hal-01655461

Publications of the year

Articles in International Peer-Reviewed Journals

• 11P. Alquier, B. Guedj.
  
  Simpler PAC-Bayesian Bounds for Hostile Data, in: Machine Learning, 2018. [ DOI : 10.1007/s10994-017-5690-0 ]
  
  https://hal.inria.fr/hal-01385064
• 12C. Biernacki, A. Lourme.
  
  Unifying Data Units and Models in (Co-)Clustering, in: Advances in Data Analysis and Classification, May 2018, vol. 12, n^o 41.
  
  https://hal.archives-ouvertes.fr/hal-01653881
• 13S. Curceac, C. Ternynck, T. B. Ouarda, F. Chebana, S. Dabo-Niang.
  
  Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models, in: Environmental Modelling and Software, January 2019, vol. 111, pp. 394-408. [ DOI : 10.1016/j.envsoft.2018.09.017 ]
  
  https://hal.inria.fr/hal-01948928
• 14C. Dhaenens, J. Jacques, V. Vandewalle, M. Vandromme, E. Chazard, C. Preda, A. Amarioarei, P. Chaiwuttisak, C. Cozma, G. Ficheur, M.-E. Kessaci, R. Perichon, J. Taillard, R. Bordet, A. Lansiaux, L. Jourdan, D. Delerue, A. Hansske.
  
  ClinMine: Optimizing the Management of Patients in Hospital, in: IRBM, January 2018, vol. 39, n^o 2, pp. 83-92. [ DOI : 10.1016/j.irbm.2017.12.002 ]
  
  https://hal.inria.fr/hal-01692197
• 15R. Giraldo, S. Dabo-Niang, S. Martinez.
  
  Statistical modeling of spatial big data: An approach from a functional data analysis perspective, in: Statistics & Probability Letters, February 2018, vol. 136, pp. 126-129. [ DOI : 10.1016/j.spl.2018.02.025 ]
  
  https://hal.archives-ouvertes.fr/hal-01744181
• 16B. Guedj, S. Robbiano.
  
  PAC-Bayesian High Dimensional Bipartite Ranking, in: Journal of Statistical Planning and Inference, 2018. [ DOI : 10.1016/j.jspi.2017.10.010 ]
  
  https://hal.inria.fr/hal-01226472
• 17B. Guedj, B. Srinivasa Desikan.
  
  Pycobra: A Python Toolbox for Ensemble Learning and Visualisation, in: Journal of Machine Learning Research, June 2018, vol. 18, pp. 1 - 5.
  
  https://hal.inria.fr/hal-01514059
• 18J. Jacques, C. Biernacki.
  
  Model-Based Co-clustering for Ordinal Data, in: Computational Statististics & Data Analysis, July 2018, vol. 123, 15 p.
  
  https://hal.inria.fr/hal-01448299
• 19L. Li, B. Guedj, S. Loustau.
  
  A Quasi-Bayesian Perspective to Online Clustering, in: Electronic journal of statistics , 2018. [ DOI : 10.1214/18-EJS1479 ]
  
  https://hal.inria.fr/hal-01264233
• 20M. Marbac, V. Vandewalle.
  
  A tractable Multi-Partitions Clustering, in: Computational Statististics & Data Analysis, July 2018. [ DOI : 10.1016/j.csda.2018.06.013 ]
  
  https://hal.inria.fr/hal-01691417

Invited Conferences

• 21C. Biernacki.
  
  Going further in cluster analysis and classification: Bi-clustering and co-clustering, in: Summer School on Clustering, Data Analysis and Visualization of Complex Data, Catania, Italy, May 2018.
  
  https://hal.inria.fr/hal-01810380
• 22C. Biernacki.
  
  Introduction to cluster analysis and classification: Evaluating clustering, in: Summer School on Clustering, Data Analysis and Visualization of Complex Data, Catania, Italy, May 2018.
  
  https://hal.inria.fr/hal-01810377
• 23C. Biernacki.
  
  Introduction to cluster analysis and classification: Performing clustering, in: Summer School on Clustering, Data Analysis and Visualization of Complex Data, Catania, Italy, May 2018.
  
  https://hal.inria.fr/hal-01810376
• 24S. Iovleff, S. N. Sylla.
  
  blockcluster, simerge and C++ with R, in: Mixture Models: Theory and Applications, Paris, France, June 2018.
  
  https://hal.inria.fr/hal-01884822
• 25V. Vandewalle, M. Marbac.
  
  A tractable multi-partitions clustering, in: COMPSTAT 2018 - 23rd International Conference on Computational Statistics, Iasi, Romania, August 2018.
  
  https://hal.inria.fr/hal-01956922

National Conferences with Proceedings

• 26M. Selosse, J. Jacques, C. Biernacki.
  
  Co-clustering de données textuelles et continues, in: SFdS 2018 - 50èmes Journées de Statistique, Saclay, France, May 2018.
  
  https://hal.inria.fr/hal-01797493

Conferences without Proceedings

• 27A.-L. Bedenel, L. Jourdan, C. Biernacki.
  
  Probabilities estimation by a genetic algorithm, in: ROADEF2018, Lorient, France, February 2018.
  
  https://hal.archives-ouvertes.fr/hal-01868195
• 28A.-L. Bedenel, L. Jourdan, C. Biernacki.
  
  Probability estimation by an adapted genetic algorithm in web insurance, in: LION 12 - Learning and Intelligent Optimization Conference, Kalamata, Greece, June 2018.
  
  https://hal.archives-ouvertes.fr/hal-01885117
• 29C. Biernacki, B. Auder, G. Celeux, J. Demont, F. Langrognet, V. Kubicki, C. Poli, J. Renault.
  
  MASSICCC: A SaaS Platform for Clustering and Co-Clustering of Mixed Data, in: Workshop MixStatSeq: “Mixture models: Theory and applications”, Paris, France, June 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949175
• 30C. Biernacki.
  
  Introduction to cluster analysis and classification: Formalizing clustering, in: Summer School on Clustering, Data Analysis and Visualization of Complex Data, Catania, Italy, May 2018.
  
  https://hal.inria.fr/hal-01810379
• 31C. Biernacki.
  
  Model selection theory and considerations in large scale scenarios, in: Research Summer School on Statistics for Data Science – S4D, Caen, France, June 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949168
• 32C. Biernacki.
  
  Model-based clustering and co-clusteringin high-dimensional scenarios, in: Research Summer School on Statistics for Data Science – S4D, Caen, France, June 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949167
• 33C. Biernacki, G. Celeux, J. Josse, F. Laporte.
  
  Dealing with missing data in model-based clustering through a MNAR model, in: CMStatistics 2018 - 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pise, Italy, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949120
• 34C. Biernacki, V. Vandewalle, M. Marbac.
  
  Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering, in: 23rd International Conference on Computational Statistics, Iasi, Romania, August 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949127
• 35A. Ehrhardt, V. Vandewalle, C. Biernacki, P. Heinrich.
  
  Supervised multivariate discretization and levels merging for logistic regression, in: 23rd International Conference on Computational Statistics, Iasi, Romania, August 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949128
• 36C. Keribin, C. Biernacki.
  
  Co-clustering: A versatile way to perform clustering in high dimension, in: The 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), Pise, Italy, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949116
• 37M. Marbac, C. Biernacki, M. Sedki, V. Vandewalle.
  
  A targeted multi-partitions clustering, in: The 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), Pise, Italy, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949111
• 38M. Selosse, J. Jacques, C. Biernacki.
  
  Analyzing large matrices of ordinal data, in: The 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), Pise, Italy, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01949095

Scientific Books (or Scientific Book chapters)

• 39S. Dabo-Niang, C. Preda, V. Vandewalle.
  
  Clustering spatial functional data, in: Geostatistical Functional Data Analysis : Theory and Methods. Editors: Jorge Mateu, Ramon Giraldo, 2018.
  
  https://hal.inria.fr/hal-01948934

Other Publications

• 40M. Baelde, C. Biernacki, R. Greff.
  
  Real-Time Monophonic and Polyphonic Audio Classification from Power Spectra, January 2019, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01834221
• 41C. Biernacki, M. Marbac, V. Vandewalle.
  
  Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering, December 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01949155
• 42A. Ehrhardt, C. Biernacki, V. Vandewalle, P. Heinrich.
  
  Feature quantization for parsimonious and interpretable predictive models, December 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01949135
• 43P. Germain, A. Habrard, F. Laviolette, E. Morvant.
  
  PAC-Bayes and Domain Adaptation, November 2018, https://arxiv.org/abs/1707.05712 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01563152
• 44A. Goyal, E. Morvant, P. Germain, M.-R. Amini.
  
  Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters, August 2018, https://arxiv.org/abs/1808.05784 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01857463
• 45Q. Grimonprez, S. Blanck, A. Celisse, G. Marot.
  
  MLGL: An R package implementing correlated variable selection by hierarchical clustering and group-Lasso, August 2018, working paper or preprint.
  
  https://hal.inria.fr/hal-01857242
• 46B. Guedj.
  
  A Primer on PAC-Bayesian Learning, January 2019, working paper or preprint.
  
  https://hal.inria.fr/hal-01983732
• 47B. Guedj, L. Li.
  
  Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly, May 2018, working paper or preprint.
  
  https://hal.inria.fr/hal-01796011
• 48A. Hiba, N. Wicker, C. Biernacki.
  
  Projection under pairwise distance controls, December 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01420662
• 49S. Iovleff, S. N. Sylla, C. Loucoubar.
  
  Block clustering of Binary Data with Gaussian Co-variables, December 2018, https://arxiv.org/abs/1812.08520 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01961978
• 50J. Klein, M. Albardan, B. Guedj, O. Colot.
  
  Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles, April 2018, https://arxiv.org/abs/1804.10028 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01779989
• 51G. Letarte, E. Morvant, P. Germain.
  
  Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior, 2018, https://arxiv.org/abs/1810.12683 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01908555
• 52G. Mazo, Y. Averyanov.
  
  Constraining kernel estimators in semiparametric copula mixture models, November 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01774629
• 53A. Poddar, S. Iovleff, F. Latimier.
  
  Estimation of Parsimonious Covariance Models for Gaussian Matrix Valued Random Variables for Multi-Dimensional Spectroscopic Data, December 2018, WiML 2018 - 13th Women in Machine Learning workshop, Poster.
  
  https://hal.archives-ouvertes.fr/hal-01954769
• 54M. Selosse, J. Jacques, C. Biernacki, F. Cousson-Gélie.
  
  Analyzing quality of life survey using constrained co-clustering model for ordinal data and some dynamic implication, July 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01643910
• 55M. Selosse, J. Jacques, C. Biernacki.
  
  Model-based co-clustering for mixed type data, October 2018, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01893457
• 56M. Selosse, J. Jacques, C. Biernacki.
  
  mixedClust: an R package for mixed data classiication, clustering and co-clustering, July 2018, 25th Summer Session Working Group on Model-Based Clustering, Poster.
  
  https://hal.archives-ouvertes.fr/hal-01949171
• 57M. Selosse, J. Jacques, C. Biernacki.
  
  ordinalClust: an R package for analyzing ordinal data, September 2018, working paper or preprint.
  
  https://hal.inria.fr/hal-01678800