Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Major publications by the team in recent years
[1]
P. 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
[2]
P. 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
[3]
C. Biernacki, A. Lourme.
Unifying Data Units and Models in (Co-)Clustering, in: Advances in Data Analysis and Classification, May 2018, vol. 12, no 41.
https://hal.archives-ouvertes.fr/hal-01653881
[4]
A. Celisse.
Optimal cross-validation in density estimation with the L2-loss, in: The Annals of Statistics, 2014, vol. 42, no 5, pp. 1879–1910.
https://hal.archives-ouvertes.fr/hal-00337058
[5]
S. Dabo-Niang, C. Ternynck, A.-F. Yao.
Nonparametric prediction in the multivariate spatial context, in: Journal of Nonparametric Statistics, 2016, vol. 28, no 2, pp. 428-458. [ DOI : 10.1080/10485252.2016.01.007 ]
https://hal.inria.fr/hal-01425932
[6]
J. 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, no 6, pp. 985–996. [ DOI : 10.1101/gr.217075.116 ]
https://hal.archives-ouvertes.fr/hal-01647846
[7]
G. Letarte, P. Germain, B. Guedj, F. Laviolette.
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks, in: NeurIPS 2019, Vancouver, Canada, December 2019.
https://hal.inria.fr/hal-02139432
[8]
M. 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
[9]
C. 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
[10]
H. Tyagi, J. Vybiral.
Learning general sparse additive models from point queries in high dimensions, in: Constructive Approximation, January 2019.
https://hal.inria.fr/hal-02379404
Publications of the year

Doctoral Dissertations and Habilitation Theses

[11]
M. Baelde.
Generative models for the classification and separation of real-time sound sources, Université de Lille 1, September 2019.
https://hal.archives-ouvertes.fr/tel-02399081
[12]
A.-L. Bedenel.
Matching descriptors evolving over time: application to insurance comparison, Université de Lille I, April 2019.
https://hal.archives-ouvertes.fr/tel-02399068
[13]
A. Ehrhardt.
Formalization and study of statistical problems in Credit Scoring : Reject inference, discretization and pairwise interactions, logistic regression trees, Université de Lille, September 2019.
https://hal.archives-ouvertes.fr/tel-02302691

Articles in International Peer-Reviewed Journals

[14]
P. Alliez, R. Di Cosmo, B. Guedj, A. Girault, M.-S. Hacid, A. Legrand, N. P. Rougier.
Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria, in: Computing in Science & Engineering, 2019, pp. 1-14, https://arxiv.org/abs/1905.11123. [ DOI : 10.1109/MCSE.2019.2949413 ]
https://hal.archives-ouvertes.fr/hal-02135891
[15]
S. Arlot, A. Celisse, Z. Harchaoui.
A Kernel Multiple Change-point Algorithm via Model Selection, in: Journal of Machine Learning Research, December 2019, vol. 20, no 162, pp. 1–56, https://arxiv.org/abs/1202.3878.
https://hal.archives-ouvertes.fr/hal-00671174
[16]
M. Baelde, C. Biernacki, R. Greff.
Real-Time Monophonic and Polyphonic Audio Classification from Power Spectra, in: Pattern Recognition, August 2019, vol. 92, pp. 82-92. [ DOI : 10.1016/j.patcog.2019.03.017 ]
https://hal.archives-ouvertes.fr/hal-01834221
[17]
M. Bernardini, A. Brossa, G. Chinigo, G. Grolez, G. Trimaglio, L. Allart, A. Hulot, G. Marot, T. Genova, A. Joshi, V. Mattot, G. Fromont, L. Munaron, B. Bussolati, N. Prevarskaya, A. Fiorio Pla, D. Gkika.
Transient Receptor Potential Channel Expression Signatures in Tumor-Derived Endothelial Cells: Functional Roles in Prostate Cancer Angiogenesis, in: Cancers, July 2019, vol. 11, no 7, 956 p. [ DOI : 10.3390/cancers11070956 ]
https://hal.archives-ouvertes.fr/hal-02404061
[18]
S. 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
[19]
M. Cuvelliez, V. Vandewalle, M. Brunin, O. Beseme, A. Hulot, P. De Groote, P. Amouyel, C. Bauters, G. Marot, F. Pinet.
Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction - Short title: Proteomic signature of early death in heart failure patients, in: Scientific Reports, 2019, forthcoming.
https://hal.inria.fr/hal-02400814
[20]
M. Cuvelliez, V. Vandewalle, M. Brunin, O. Beseme, A. Hulot, P. De Groote, P. Amouyel, C. Bauters, G. Marot, F. Pinet.
Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction, in: Scientific Reports, December 2019, vol. 9, 19202 p. [ DOI : 10.1038/s41598-019-55727-1 ]
https://hal.archives-ouvertes.fr/hal-02414293
[21]
S. Dabo-Niang, S. Curceac, C. Ternynck, T. B. Ouarda, F. Chebana, S. D. 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-02334991
[22]
S. Dabo-Niang, B. Thiam.
Kernel regression estimation with errors-in-variables for random fields, in: Afrika Matematika, 2019. [ DOI : 10.1007/s13370-019-00654-7 ]
https://hal.inria.fr/hal-02334993
[23]
F. Dewez, V. Montmirail.
Decrypting the Hill Cipher via a Restricted Search over the Text-Space, in: Linköping Electronic Conference Proceedings, June 2019.
https://hal.univ-cotedazur.fr/hal-02271395
[24]
A. Eftekhari, J. Tanner, A. Thompson, B. Toader, H. Tyagi.
Sparse non-negative super-resolution — simplified and stabilised, in: Applied and Computational Harmonic Analysis, August 2019. [ DOI : 10.1016/j.acha.2019.08.004 ]
https://hal.inria.fr/hal-02379445
[25]
P. Germain, A. Habrard, F. Laviolette, E. Morvant.
PAC-Bayes and Domain Adaptation, in: Neurocomputing, 2020, vol. 379, pp. 379-397, https://arxiv.org/abs/1707.05712. [ DOI : 10.1016/j.neucom.2019.10.105 ]
https://hal.archives-ouvertes.fr/hal-01563152
[26]
A. Goyal, E. Morvant, P. Germain, M.-R. Amini.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters, in: Neurocomputing, 2019, https://arxiv.org/abs/1808.05784, forthcoming. [ DOI : 10.1016/j.neucom.2019.04.072 ]
https://hal.archives-ouvertes.fr/hal-01857463
[27]
D. A. Mogilenko, J. Haas, L. L'homme, S. Fleury, S. Quemener, M. Levavasseur, C. Becquart, J. Wartelle, A. Bogomolova, L. Pineau, O. Molendi-Coste, S. Lancel, H. Dehondt, C. Gheeraert, A. Melchior, C. Dewas, A. Nikitin, S. Pic, N. Rabhi, J.-S. Annicotte, S. Oyadomari, T. Velasco-Hernandez, J. Cammenga, M. Foretz, B. Viollet, M. Vukovic, A. Villacreces, K. Kranc, P. Carmeliet, G. Marot, A. Boulter, S. J. Tavernier, L. Berod, M. P. Longhi, C. Paget, S. Janssens, D. Staumont-Sallé, E. Aksoy, B. Staels, D. Dombrowicz.
Metabolic and innate immune cues merge into a specific inflammatory response via unfolded proteinresponse (UPR), in: Cell, May 2019, vol. 177, no 5, pp. 1201-1216.e19, Erratum in : Metabolic and Innate Immune Cues Merge into a Specific Inflammatory Response via the UPR. [Cell. 2019], forthcoming. [ DOI : 10.1016/j.cell.2019.03.018 ]
https://www.hal.inserm.fr/inserm-02084447
[28]
M. Selosse, J. Jacques, C. Biernacki, F. Cousson-Gélie.
Analysing a quality of life survey using a co-clustering model for ordinal data and some dynamic implications, in: Journal of the Royal Statistical Society: Series C Applied Statistics, July 2019.
https://hal.archives-ouvertes.fr/hal-01643910
[29]
M. Selosse, J. Jacques, C. Biernacki.
Model-based co-clustering for mixed type data, in: Computational Statistics and Data Analysis, 2020, vol. 144, 106866 p. [ DOI : 10.1016/j.csda.2019.106866 ]
https://hal.archives-ouvertes.fr/hal-01893457
[30]
H. Tyagi, J. Vybiral.
Learning general sparse additive models from point queries in high dimensions, in: Constructive Approximation, January 2019.
https://hal.inria.fr/hal-02379404

Invited Conferences

[31]
C. Biernacki, G. Celeux, J. Josse, F. Laporte.
Dealing with missing data in model-based clustering through a MNAR model, in: CRoNos & MDA 2019 - Meeting and Workshop on Multivariate Data Analysis and Software, Limassol, Cyprus, April 2019.
https://hal.inria.fr/hal-02103347

International Conferences with Proceedings

[32]
M. Cucuringu, P. Davies, A. Glielmo, H. Tyagi.
SPONGE: A generalized eigenproblem for clustering signed networks, in: AISTATS, Okinawa, Japan, April 2019.
https://hal.inria.fr/hal-02379505
[33]
B. Guedj, J. Rengot.
Non-linear aggregation of filters to improve image denoising, in: Computing Conference 2020, London, United Kingdom, July 2020.
https://hal.inria.fr/hal-02086856
[34]
J. Klein, M. Albardan, B. Guedj, O. Colot.
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles, in: ECML-PKDD, Decentralized Machine Learning at the Edge Workshop, Wurzburg, Germany, September 2019, https://arxiv.org/abs/1804.10028.
https://hal.archives-ouvertes.fr/hal-01779989
[35]
G. Letarte, P. Germain, B. Guedj, F. Laviolette.
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks, in: NeurIPS 2019, Vancouver, Canada, December 2019.
https://hal.inria.fr/hal-02139432
[36]
G. Letarte, E. Morvant, P. Germain.
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior, in: The 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019,, 2019, https://arxiv.org/abs/1810.12683.
https://hal.archives-ouvertes.fr/hal-01908555
[37]
Z. Mhammedi, P. Grünwald, B. Guedj.
PAC-Bayes Un-Expected Bernstein Inequality, in: NeurIPS 2019, Vancouver, Canada, December 2019, https://arxiv.org/abs/1905.13367.
https://hal.inria.fr/hal-02401295
[38]
V. Shalaeva, A. Fakhrizadeh Esfahani, P. Germain, M. Petreczky.
Improved PAC-Bayesian Bounds for Linear Regression, in: Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, United States, February 2020, https://arxiv.org/abs/1912.03036.
https://hal.inria.fr/hal-02396556

Conferences without Proceedings

[39]
C. Biernacki.
MASSICCC: A SaaS Platform for Clustering and Co-Clustering of Mixed Data, in: APSEM 2019 ((Apprentissage et SEMantique)) : éco-systèmes pour la science ouverte et recherche par les données, Toulouse, France, October 2019.
https://hal.archives-ouvertes.fr/hal-02399180
[40]
C. Biernacki, M. Corréard.
Predictive maintenance solution without additional sensors, in: Forum TERATEC, Palaiseau, France, June 2019.
https://hal.archives-ouvertes.fr/hal-02399046
[41]
C. Biernacki, A. Lourme.
Unifying Data Units and Models in (Co-)Clustering, in: CLADAG 2019 - 12th Scientific Meeting Classification and Data Analysis Group, Cassino, Italy, September 2019.
https://hal.archives-ouvertes.fr/hal-02398982
[42]
C. Biernacki, M. Marbac, V. Vandewalle.
Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering, in: 3rd International Conference on Econometrics and Statistics (EcoSta 2019), Taichung, Taiwan, June 2019.
https://hal.archives-ouvertes.fr/hal-02398999
[43]
A. Constantin, M. Fauvel, S. Girard, S. Iovleff, Y. Tanguy.
Classification de Signaux Multidimensionnels Irrégulièrement Echantillonnés, in: 2019 - Journée Jeunes Chercheurs MACLEAN du GDR MADICS, Paris, France, December 2019, pp. 1-2.
https://hal.archives-ouvertes.fr/hal-02394120
[44]
L. Gautheron, P. Germain, A. Habrard, G. Letarte, E. Morvant, M. Sebban, V. Zantedeschi.
Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêts, in: CAp 2019 - Conférence sur l'Apprentissage automatique, Toulouse, France, July 2019.
https://hal.archives-ouvertes.fr/hal-02148600
[45]
C. Keribin, C. Biernacki.
Co-clustering: model based or model free approaches, in: ISI WSC 2019 - 62nd ISI World Statistics Congress, Kuala Lumpur, Malaysia, August 2019.
https://hal.archives-ouvertes.fr/hal-02399031
[46]
C. Keribin, C. Biernacki.
Le modèle des blocs latents, une méthode régularisée pour la classification en grande dimension, in: JdS 2019 - 51èmes Journées de Statistique de la SFdS, Nancy, France, June 2019.
https://hal.archives-ouvertes.fr/hal-02391379
[47]
F. Laporte, C. Biernacki, G. Celeux, J. Josse.
Modèles de classification non supervisée avec données manquantes non au hasard, in: JdS 2019 - 51e journées de statistique de la Sfds, Nancy, France, June 2019.
https://hal.archives-ouvertes.fr/hal-02398984
[48]
M. Marbac-Lourdelle, C. Biernacki, V. Vandewalle.
Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering, in: SPSR 2019, Bucarest, Romania, April 2020.
https://hal.archives-ouvertes.fr/hal-02400486
[49]
V. Vandewalle, C. Ternynck, G. Marot.
Linking different kinds of Omics data through a model-based clustering approach, in: IFCS 2019, Thessalonique, Greece, August 2019.
https://hal.archives-ouvertes.fr/hal-02400525
[50]
P. Viallard, R. Emonet, P. Germain, A. Habrard, E. Morvant.
Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory, in: Workshop on Machine Learning with guarantees @ NeurIPS 2019, Vancouver, Canada, 2019.
https://hal.archives-ouvertes.fr/hal-02335762
[51]
L. Zhang, C. Biernacki, P. Germain, Y. Kessaci.
Domain Adaptation from a Pre-trained Source Model: Application on fraud detection tasks, in: 12th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2019), London, United Kingdom, December 2019.
https://hal.archives-ouvertes.fr/hal-02399003

Scientific Books (or Scientific Book chapters)

[52]
S. Dabo-Niang, S. MANOU-ABI, S. Jean-Jacques.
Mathematical Modeling and Study of Random or Deterministic Phenomena,, Wiley, 2020.
https://hal.inria.fr/hal-02334997

Other Publications

[53]
H. Alawieh, N. Wicker, C. Biernacki.
Projection under pairwise distance controls, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01420662
[54]
C. Biernacki, M. Marbac, V. Vandewalle.
Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01949155
[55]
S. Chrétien, B. Guedj.
Revisiting clustering as matrix factorisation on the Stiefel manifold, March 2019, https://arxiv.org/abs/1903.04479 - working paper or preprint.
https://hal.inria.fr/hal-02064396
[56]
S. Chrétien, H. Tyagi.
Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method, November 2019, working paper or preprint.
https://hal.inria.fr/hal-02379598
[57]
V. Cohen-Addad, B. Guedj, V. Kanade, G. Rom.
Online k-means Clustering, December 2019, https://arxiv.org/abs/1909.06861 - 11 pages, 1 figure.
https://hal.inria.fr/hal-02401290
[58]
A. Constantin, M. Fauvel, S. Girard, S. Iovleff.
Classification de Signaux Multidimensionnels Irrégulièrement Échantillonnés, August 2019, GRETSI 2019 - 27e Colloque francophone de traitement du signal et des images, Poster.
https://hal.archives-ouvertes.fr/hal-02276255
[59]
A. Constantin, M. Fauvel, S. Girard, S. Iovleff.
Supervised classification of multidimensional and irregularly sampled signals, April 2019, 1 p, Statlearn 2019 - Workshop on Challenging problems in Statistical Learning, Poster.
https://hal.archives-ouvertes.fr/hal-02092347
[60]
M. Cucuringu, H. Tyagi.
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping, November 2019, working paper or preprint.
https://hal.inria.fr/hal-02379573
[61]
A. D'Aspremont, M. Cucuringu, H. Tyagi.
Ranking and synchronization from pairwise measurements via SVD, October 2019, https://arxiv.org/abs/1906.02746 - 42 pages, 9 figures.
https://hal.archives-ouvertes.fr/hal-02340372
[62]
A. Ehrhardt, C. Biernacki, V. Vandewalle, P. Heinrich.
Feature quantization for parsimonious and interpretable predictive models, March 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01949135
[63]
M. P. B. Gallaugher, C. Biernacki, P. D. McNicholas.
Parameter-Wise Co-Clustering for High-Dimensional Data, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01862824
[64]
L. Gautheron, P. Germain, A. Habrard, E. Morvant, M. Sebban, V. Zantedeschi.
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting, June 2019, https://arxiv.org/abs/1906.06203 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02148618
[65]
B. Guedj, B. S. Desikan.
Kernel-Based Ensemble Learning in Python, December 2019, https://arxiv.org/abs/1912.08311 - 11 pages.
https://hal.inria.fr/hal-02443097
[66]
B. Guedj.
A Primer on PAC-Bayesian Learning, May 2019, working paper or preprint.
https://hal.inria.fr/hal-01983732
[67]
B. Guedj, L. Li.
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly, May 2019, working paper or preprint.
https://hal.inria.fr/hal-01796011
[68]
B. Guedj, L. Pujol.
Still no free lunches: the price to pay for tighter PAC-Bayes bounds, December 2019, https://arxiv.org/abs/1910.04460 - working paper or preprint.
https://hal.inria.fr/hal-02401286
[69]
F. Laporte, C. Biernacki, G. Celeux, J. Josse.
Model-based clustering with missing not at random data. Missing mechanism, July 2019, Working Group on Model-Based Clustering Summer Session, Poster.
https://hal.archives-ouvertes.fr/hal-02398987
[70]
G. Mazo, Y. Averyanov.
Constraining kernel estimators in semiparametric copula mixture models, March 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01774629
[71]
K. Nozawa, P. Germain, B. Guedj.
PAC-Bayesian Contrastive Unsupervised Representation Learning, December 2019, https://arxiv.org/abs/1910.04464 - working paper or preprint.
https://hal.inria.fr/hal-02401282
[72]
M. Selosse, J. Jacques, C. Biernacki.
Textual data summarization using the Self-Organized Co-Clustering model, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02115294
[73]
M. Selosse, J. Jacques, C. Biernacki.
ordinalClust: an R package for analyzing ordinal data, December 2019, working paper or preprint.
https://hal.inria.fr/hal-01678800
[74]
S. N. Sylla, S. Dabo-Niang, C. Loucoubar.
Functional data analysis of parasite densities in the Senegalese villages of Dielmo and NDiop, October 2019, working paper or preprint.
https://hal.inria.fr/hal-02335001
[75]
J. Zhang, E. T. Barr, B. Guedj, M. Harman, J. Shawe-Taylor.
Perturbed Model Validation: A New Framework to Validate Model Relevance, May 2019, working paper or preprint.
https://hal.inria.fr/hal-02139208