Publications HAL

Journal articles

2021

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Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. Learning from missing data with the Latent Block Model. Statistics and Computing, 2021, 32, pp.9. ⟨10.1007/s11222-021-10058-y⟩. ⟨hal-02973814⟩
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2020

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Xuhong Li, Yves Grandvalet, Franck Davoine. A baseline regularization scheme for transfer learning with convolutional neural networks. Pattern Recognition, 2020, 98, pp.107049. ⟨10.1016/j.patcog.2019.107049⟩. ⟨hal-02315752⟩
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Xuhong Li, Yves Grandvalet, Franck Davoine, Jingchun Cheng, Yin Cui, et al.. Transfer Learning in Computer Vision Tasks: Remember Where You Come From. Image and Vision Computing, 2020, 93, pp.103853. ⟨10.1016/j.imavis.2019.103853⟩. ⟨hal-02988362⟩
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2018

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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Model Selection for Gaussian Latent Block Clustering with the Integrated Classification Likelihood. Advances in Data Analysis and Classification, 2018, 12 (3), pp.489-508. ⟨10.1007/s11634-013-0161-3⟩. ⟨hal-00913680⟩
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2017

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Jean-Michel Bécu, Yves Grandvalet, Christophe Ambroise, Cyril Dalmasso. Beyond support in two-stage variable selection. Statistics and Computing, 2017, 27 (1), pp.169--179. ⟨10.1007/s11222-015-9614-1⟩. ⟨hal-01246066⟩
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2016

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Julien Chiquet, Yves Grandvalet, Guillem Rigaill. On coding effects in regularized categorical regression. Statistical Modelling, 2016, 16 (3), pp.228-237. ⟨10.1177/1471082X16644998⟩. ⟨hal-01338164⟩
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Alberto García-Durán, Antoine Bordes, Nicolas Usunier, Yves Grandvalet. Combining Two and Three-Way Embedding Models for Link Prediction in Knowledge Bases. Journal of Artificial Intelligence Research, 2016, 55, pp.715--742. ⟨10.1613/jair.5013⟩. ⟨hal-01313319⟩
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2015

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Xiao Liu, Antoine Bordes, Yves Grandvalet. Extracting biomedical events from pairs of text entities. BMC Bioinformatics, 2015, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩. ⟨hal-01313324⟩
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Marta Avalos, Hélène Pouyes, Yves Grandvalet, Ludivine Orriols, Emmanuel Lagarde. Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm. BMC Bioinformatics, 2015, 16 (Suppl 6), pp.S1. ⟨10.1186/1471-2105-16-S6-S1⟩. ⟨hal-01217312⟩
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2014

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Marta Avalos, Ludivine Orriols, Hélène Pouyes, Yves Grandvalet, Frantz Thiessard, et al.. Variable selection on large case-crossover data: application to a registry-based study of prescription drugs and road traffic crashes.. Pharmacoepidemiology and Drug Safety, 2014, pp.140-51. ⟨10.1002/pds.3539⟩. ⟨hal-01099301⟩
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2012

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Marta Avalos, Yves Grandvalet, N. Duran Adroher, Ludivine Orriols, Emmanuel Lagarde. Analysis of multiple exposures in the case-crossover design via sparse conditional likelihood. Statistics in Medicine, 2012, 31 (21), pp.2290-2302. ⟨10.1002/sim.5344⟩. ⟨hal-00742310⟩
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Julien Chiquet, Yves Grandvalet, Camille Charbonnier. Sparsity with sign-coherent groups of variables via the cooperative-lasso. Annals of Applied Statistics, 2012, 6 (2), pp.795-830. ⟨10.1214/11-AOAS520⟩. ⟨hal-00707281⟩
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Marta Avalos, N. Duran Adroher, Emmanuel Lagarde, Frantz Thiessard, Yves Grandvalet, et al.. Prescription-Drug-Related Risk in Driving: Comparing Conventional and Lasso Shrinkage Logistic Regressions. Epidemiology, 2012, 23 (5), pp.706-712. ⟨10.1097/EDE.0b013e31825fa528⟩. ⟨hal-00742317⟩
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2011

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Julien Chiquet, Yves Grandvalet, Christophe Ambroise. Inferring Multiple Graphical Structures. Statistics and Computing, 2011, 21 (4), pp.537-553. ⟨10.1007/s11222-010-9191-2⟩. ⟨hal-00660169⟩
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Conference papers

2021

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Abdelhak Loukkal, Yves Grandvalet, Tom Drummond, You Li. Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning. IEEE Winter Conference on Applications of Computer Vision (WACV 2021), Jan 2021, Waikoloa, United States. pp.51--60, ⟨10.1109/WACV48630.2021.00010⟩. ⟨hal-02913515⟩
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2019

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Abdelhak Loukkal, Yves Grandvalet, You Li. Disparity weighted loss for semantic segmentation of driving scenes. 22nd IEEE International Conference on Intelligent Transportation Systems (ITSC 2019), Oct 2019, Auckland, New Zealand. pp.3427-3432, ⟨10.1109/ITSC.2019.8917171⟩. ⟨hal-02465013⟩
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Filippo Cara, Nassim Boudaoud/, Yves Grandvalet, Amélie Ponchet-Durupt. Quality parts prediction in industry 4.0: a first attemps towards product failures anticipation. 13ème Conférence internationale CIGI QUALITA 2019, Jun 2019, Montréal Québec, Canada. ⟨hal-04235656⟩
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Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. Données manquantes dans un modèle à blocs latents pour la recommandation. 51es Journées de statistique de la Société Française de Statistique (SFdS - jds 2019), Jun 2019, Nancy, France. ⟨hal-02484713⟩
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2018

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Abdelhak Loukkal, Vincent Frémont, Yves Grandvalet, You Li. Improving semantic segmentation in urban scenes with a cartographic information. 15th International Conference on Control, Automation, Robotics and Vision (ICARCV 2018), Nov 2018, Singapore, Singapore. pp.400-406, ⟨10.1109/ICARCV.2018.8581165⟩. ⟨hal-01875096⟩
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Xuhong Li, Yves Grandvalet, Franck Davoine. Explicit Inductive Bias for Transfer Learning with Convolutional Networks. 35th International Conference on Machine Learning (ICML 2018), Jul 2018, Stockholm, Sweden. pp.2825-2834. ⟨hal-01843169⟩
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Xuhong Li, Franck Davoine, Yves Grandvalet. A Simple Weight Recall for Semantic Segmentation: Application to Urban Scenes. 29th IEEE Intelligent Vehicles Symposium (IV 2018), Jun 2018, Changshu, Suzhou, China. pp.1007-1012, ⟨10.1109/IVS.2018.8500680⟩. ⟨hal-01838445⟩
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2016

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Shameem Ahamed Puthiya Parambath, Nicolas Usunier, Yves Grandvalet. A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. 10th ACM Conference on Recommender Systems (RecSys '16), Sep 2016, Boston, United States. pp.15--22, ⟨10.1145/2959100.2959149⟩. ⟨hal-01387171⟩
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2015

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Jean-Michel Bécu, Yves Grandvalet, Christophe Ambroise, Cyril Dalmasso. Significance testing for variable selection in high-dimension. Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Aug 2015, Niagara Falls, Canada. pp.1-8, ⟨10.1109/CIBCB.2015.7300313⟩. ⟨hal-01313310⟩
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2014

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Shameem Puthiya A. Parambath, Nicolas Usunier, Yves Grandvalet. Optimizing F-Measures by Cost-Sensitive Classification. Advances in Neural Information Processing Systems 27, Dec 2014, Montréal, Canada. ⟨hal-01196627⟩
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Xiao Liu, Antoine Bordes, Yves Grandvalet. Fast recursive multi-class classification of pairs of text entities for biomedical event extraction. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2014, Gothenburg, Sweden. pp.692--701. ⟨hal-01060830⟩
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2013

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Xiao Liu, Antoine Bordes, Yves Grandvalet. Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities. BioNLP Shared Task 2013 Workshop, Aug 2013, Sofia, Bulgaria. pp.45-49. ⟨hal-00880444⟩
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Marta Avalos, Yves Grandvalet, Hélène Pouyes, Ludivine Orriols, Emmanuel Lagarde. High–Dimensional Sparse Matched Case–Control and Case–Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies. CIBB 2013 - 10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Jun 2013, Nice, France. pp.109-124. ⟨hal-01099313⟩
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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Critères ICL pour la sélection de modèle pour la classification croisée de données continues. 45e Journées de Statistique (JdS 2013), May 2013, Toulouse, France. pp.1-6. ⟨hal-00933356⟩
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Marta Fernandez Avalos, Yves Grandvalet, Hélène Pouyes, Ludivine Orriols, Emmanuel Lagarde. clogitLasso: an R package for high–dimensional analysis of matched case–control and case–crossover data. CIBB & PRIB 2013, 2013, Nice, France. ⟨hal-01578291⟩
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Aurore Lomet, Gérard Govaert, Yves Grandvalet. An approximation of the integrated classification likelihood for the latent block model. IEEE International Conference on Data Mining series (ICDM 2012), Workshop on Data Mining, 2013, Bruxelles, Belgium. pp.147-153. ⟨hal-00933245⟩
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2012

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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Model selection in block clustering by the integrated classification likelihood. 20th International Conference on Computational Statistics (COMPSTAT 2012), Aug 2012, Lymassol, France. pp.519-530. ⟨hal-00730829⟩
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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Integrated classification likelihood for model selection in block clustering. Workshop statistical inference in complex/high-dimensional problems, Jul 2012, Vienne, Austria. pp.1-16. ⟨hal-00933256⟩
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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Un protocole de simulation de données pour la classification croisée. 44e Journées de Statistique, SFdS, May 2012, Bruxelles, Belgique. pp.1-6. ⟨hal-00933371⟩
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L. F. Sànchez Merchante, Yves Grandvalet, Gérard Govaert. An Efficient Approach to Sparse Linear Discriminant Analysis. ICML 2012, 2012, France, France. pp.1167-1174. ⟨hal-00742355⟩
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Patents

2015

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Stephane Canu, Jérôme Fournier, Yves Grandvalet, Benjamin Labbé, Gaëlle Loosli, et al.. Procédé d'élaboration d'un discriminateur multiclasse. France, N° de brevet: FR2993381. 2015. ⟨hal-01593585⟩
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Preprints, Working Papers, ...

2021

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Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. SparseBM: A Python Module for Handling Sparse Graphs with Block Models. 2021. ⟨hal-03139586⟩
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2015

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Jean-Michel Bécu, Yves Grandvalet, Christophe Ambroise, Cyril Dalmasso. Beyond Support in Two-Stage Variable Selection. 2015. ⟨hal-01145426⟩
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