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, 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
• 4A. 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
• 5S. 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
• 6J. 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
• 7G. 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
• 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
• 9C. 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
• 10H. 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

• 11M. 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
• 12A.-L. Bedenel.
  
  Matching descriptors evolving over time: application to insurance comparison, Université de Lille I, April 2019.
  
  https://hal.archives-ouvertes.fr/tel-02399068
• 13A. 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

• 14P. 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
• 15S. Arlot, A. Celisse, Z. Harchaoui.
  
  A Kernel Multiple Change-point Algorithm via Model Selection, in: Journal of Machine Learning Research, December 2019, vol. 20, n^o 162, pp. 1–56, https://arxiv.org/abs/1202.3878.
  
  https://hal.archives-ouvertes.fr/hal-00671174
• 16M. 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
• 17M. 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, n^o 7, 956 p. [ DOI : 10.3390/cancers11070956 ]
  
  https://hal.archives-ouvertes.fr/hal-02404061
• 18S. 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
• 19M. 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
• 20M. 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
• 21S. 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
• 22S. 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
• 23F. 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
• 24A. 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
• 25P. 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
• 26A. 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
• 27D. 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, n^o 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
• 28M. 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
• 29M. 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
• 30H. 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

• 31C. 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

• 32M. 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
• 33B. 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
• 34J. 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
• 35G. 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
• 36G. 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
• 37Z. 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
• 38V. 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

• 39C. 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
• 40C. Biernacki, M. Corréard.
  
  Predictive maintenance solution without additional sensors, in: Forum TERATEC, Palaiseau, France, June 2019.
  
  https://hal.archives-ouvertes.fr/hal-02399046
• 41C. 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
• 42C. 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
• 43A. 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
• 44L. 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
• 45C. 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
• 46C. 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
• 47F. 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
• 48M. 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
• 49V. 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
• 50P. 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
• 51L. 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)

• 52S. 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

• 53H. Alawieh, N. Wicker, C. Biernacki.
  
  Projection under pairwise distance controls, December 2019, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01420662
• 54C. 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
• 55S. 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
• 56S. 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
• 57V. 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
• 58A. 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
• 59A. 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
• 60M. 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
• 61A. 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
• 62A. 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
• 63M. 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
• 64L. 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
• 65B. 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
• 66B. Guedj.
  
  A Primer on PAC-Bayesian Learning, May 2019, working paper or preprint.
  
  https://hal.inria.fr/hal-01983732
• 67B. 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
• 68B. 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
• 69F. 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
• 70G. Mazo, Y. Averyanov.
  
  Constraining kernel estimators in semiparametric copula mixture models, March 2019, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01774629
• 71K. 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
• 72M. 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
• 73M. 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
• 74S. 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
• 75J. 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