Publications
2023Florian Baud and Alex Aussem. Non-Parametric Memory Guidance for Multi-Document Summarization, Recent Advances in NLP (RANLP), Varna, Burgaria, 2023.
Florian Baud and Alex Aussem. Answering student queries with a supervised memory conversational agent. The International FLAIRS Conference Proceedings, 36(1), May 2023. doi: 10.32473/flairs.36.133195.
Florian Baud and Alex Aussem. Resume automatique multi-documents guide par une base de resumes similaires. In Christophe Servan and Anne Vilnat, editors, Conference sur le Traitement Automatique des Langues Naturelles TALN 2023: 19-27, Paris, France.
Guillaume Lefebvre, Haytham Elghazel, Theodore Guillet, Alex Aussem, Matthieu Sonnati: BERTEPro : Une nouvelle approche de representation semantique dans le domaine de l'education et de la formation professionnelle. EGC 2023: 211-222.
Miguel Palencia-Olivar, Stephane Bonnevay, Alex Aussem, Bruno Canitia: Topic modeling neuronal non-parametrique pour l'extraction d'insight client : une application a l'industrie du pneumatique. EGC 2023: 499-506.
Florian Baud, Alex Aussem: Repondre aux requetes des etudiants avec un agent conversationnel a memoire supervisee. EGC 2023: 631-632.
Guillaume Lefebvre, Haytham Elghazel, Theodore Guillet, Alex Aussem, Matthieu Sonnati: BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain. SAC 2023: 929-935.
2022
M. Palencia-Olivar, S. Bonnevay, A. Aussem, B. Canitia. Nonparametric neural topic modeling for customer insight extraction about the tire industry. IJCNN 2022: 1-9.
M. Palencia-Olivar, S. Bonnevay, A. Aussem, Bruno Canitia: Processus de Dirichlet profonds pour le topic modeling. EGC 2022: 355-362.
2021
C. Sage, T. Douzon, A. Aussem, V. Eglin, H. Elghazel, S. Duffner, C. Garcia, J. Espinas: Data-Efficient Information Extraction from Documents with Pre-trained Language Models. ICDAR Workshops (2) 2021: 455-469
M. Palencia-Olivar, S. Bonnevay, A. Aussem, B. Canitia: Neural Embedded Dirichlet Processes for Topic Modeling. MDAI 2021: 299-310 .
2020
L. Guo, S. Boukir, A. Aussem: Building bagging on critical instances. Expert Syst. J. Knowl. Eng. 37(2) (2020)
C. Sage, A. Aussem, V. Eglin, H. Elghazel, J Espinas: End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks. SPNLP@EMNLP 2020: 43-52
2019
C. Sage, A. Aussem, H. Elghazel, V. Eglin and J. Espinas. Recurrent Neural Network Approach for Table Field Extraction in Business Documents. International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, September 20-25, 2019.
J-B. Aujogue and Alex Aussem. Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots. International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019. 1-8.
2018
D. Lecoeuche, A. Aussem and M. Gasse. On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. INNS Conference on Big Data and Deep Learning (INNS BDDL), April 17 ??? 19, Sanur, Indonesia, 2018.
2017
A. Narassiguin, H. Elghazel and A. Aussem. Dynamic Ensemble Selection with Probabilistic Classifier Chains. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 169-186, 2017.
Van-Tinh Tran and Alex Aussem. Reducing variance due to importance weighting in covariate shift bias correction . European Symposium on Artificial Neural Networks, (ESANN), Bruges, Belgium, April 26-28, 2017.
2016
M. Gasse and Alex Aussem. F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 619-631, 2016.
M. Gasse and Alex Aussem. Identifying the irreducible disjoint factors of a multivariate probability distribution. Probabilistic Graphical Models, 183-194, 2016.
H. Elghazel, A. Aussem, O. Gharroudi and W. Saadaoui.Ensemble Multi-label Text Categorization based on Rotation Forest and Latent Semantic Indexing, Expert Systems with Applications, Elsevier, 57: 1-11, 2016.
A. Narassiguin, M. Bibimoune, H. Elghazel and A. Aussem. An Extensive Empirical Comparison of Ensemble Learning Methods for Binary Classification, Pattern Analysis and Applications, Springer, 19(4): 1093-1128, 2016.
O.Gharroudi, H. Elghazel and A. Aussem: A Semi-Supervised Ensemble Approach for Multi-label Learning. ICDM Workshops 1197-1204, 2016.
A. Narassiguin, H. Elghazel, A. Aussem. Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach. ICDM Workshops, 1243-1250, 2016.
F. Magrangeas, R. Kuiper, H. Avet-Loiseau, W. Gourraud, C. Gu??rin-Charbonnel, L. Ferrer, A. Aussem, H. Elghazel, J. Suhard, H. Der Sarkissian, M. Attal, N. Munshi, P. Sonneveld, C. Dumontet, P. Moreau, M. Van Duin, L. Campion and S. Minvielle. A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Multiple Myeloma Patients, Clinical Cancer Research, 2016. doi: 10.1158/1078-0432.CCR-15-3163.
2015
M. Gasse, A. Aussem, H. Elghazel. On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. International Conference on Machine Learning (ICML), pages 2531-2539, 2015.
H. Elghazel and A. Aussem. Unsupervised Feature Selection with Ensemble Learning, Machine Learning 98(1-2): 157-180, 2015.
T. Tranh, A. Aussem. A Practical Approach to Reduce the Learning Bias under Covariate Shift. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), September 7-11, pages 71-86, 2015.
P. Caillet, S. Klemm, M. Ducher, A. Aussem, AM. Schott. Hip fracture in the elderly: a re-analysis of the EPIDOS Study with causal Bayesian Networks, Plos One, 10(3), 2015, doi: 10.1371/journal.pone.0120125.
T. Tran, A. Aussem. Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation. ICONIP, 111-118, 2015
M. Gasse, A. Aussem, H. Elghazel. On the Factorization of the Label Conditional Distribution in the context of Multi-Label Classification. International Workshop on Big Multi-Target Prediction, at ECML-PKDD 2015.
O. Gharroudi, H. Elghazel and A. Aussem. Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Italy, November 9-11, 2015. IEEE Computer Society, pages 377-384.
O. Gharroudi, H. Elghazel and A. Aussem. Calibrated k-labelsets for Ensemble Multi-Label Classification. International Conference on Neural Information Processing (ICONIP), Istanbul, Turkey, November 9-12, 2015. Lecture Notes in Computer Science - ?? Springer Verlag, pages 573-582.
2014
M. Gasse, A. Aussem and H. Elghazel. A hybrid algorithm for Bayesian network structure learning with application to multi-label learning, Expert Systems With Applications 41(15): 6755-6772, 2014.
Aussem, P. Caillet, Z. Klemm, M. Gasse, A.M. Schott, and M. Ducher. Analysis of risk factors of hip fracture with causal Bayesian networks. International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), pages 1074-1085, 2014.
O. Gharroudi, H. Elghazel and A. Aussem. A Comparison of Multi-label Feature Selection Methods using the Random Forest Paradigm, Canadian Conference on Artificial Intelligence (AI), pages 95-106, 2014.
2013
E. Prestat, S. Rodrigues de Morais, J. Vendrell, A. Thollet, C. Gautier, P. Cohen, A. Aussem. Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours. Computers in Biology and Medicine, 43(4): 334-341, 2013.2012
A. Aussem, S. Rodrigues de Morais and M. Corbex. Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks, Artificial Intelligence in Medicine, Vol. 54, pages 53-62, 2012.M. Gasse, A. Aussem, H. Elghazel. An experimental comparison of hybrid algorithms for Bayesian network structure learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pages 58-73, 2012.
F. Bellal, H. Elghazel and A. Aussem. A semi supervised feature ranking method with ensemble learning, Pattern Recognition Letters 33(10), pages 1426-1432, 2012.
2011
H. Barkia, H. Elghazel and A. Aussem. Semi-supervised feature importance evaluation with ensemble learning. IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, December 11-14, 2011, IEEE Computer Society, pages 31-40, 2011.
H. Elghazel, A. Aussem and F. Perraud. Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests, In Ensembles in Machine Learning Applications, Studies in Computational Intelligence, Springer Verlag, O. Okun, G. Valentini and M. Re (Eds), pages 169-179, 2011.
Before 2010
S. Rodrigues De Morais and A. Aussem. A novel Markov boundary based feature subset selection algorithm, Neurocomputing, Vol. 73, pages 578-584, 2010.
A. Aussem and S. R. de Morais. A conservative feature subset selection algorithm with missing data, Neurocomputing, Vol. 73, pages 585-590, 2010.
A. Aussem. Editorial: Special issue on Bayesian networks, Neurocomputing, Vol. 73, pages 561-562, 2010.
A. Aussem, A. Tchernof , S. Rodrigues de Morais and S. Rome. Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian networks, BMC Bioinformatics 11:487, 2010.
H. Elghazel and A. Aussem. Feature selection for unsupervised learning using random cluster ensembles. IEEE International Conference on Data Mining (ICDM 2010), Sydney Australia, pages 168-175, 2010.
S. Rodrigues de Morais and A. Aussem. An efficient learning algorithm for local Bayesian network structure discovery. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), LNCS Volume 6323, pages 164-169, 2010.
Z. Kebaili and A. Aussem. A novel hybrid Bayesian network structure learning algorithm based on correlated itemset mining techniques, International Journal of Computational Intelligence Research, Vol. 5, No. 1, pages 16-21, 2009.
F. Rafamantanantsoa, P. Laurencot and A. Aussem. Performance Modelling and Analysis of a Web Server, International Journal on Computer Network and Internet Research, Vol. 9, No. 2, pages 31-35, 2009.
F. Rafamantanantsoa, P. Laurencot and A. Aussem. Mixed Neural and Feedback Controller for Apache Web Server, International Journal on Computer Network and Internet Research, Vol. 9, No. 2, pages 25-30, 2009.
A. Aussem and S. R. de Morais and M. Corbex. Graph-based analysis of nasopharyngeal carcinoma with Bayesian network learning methods. 7th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition (GBR), Venice, Italy, LNCS 5534-Springer Verlag, pages 52-61, 2009.
A. Aussem, S. R. de Morais, F. Perraud and S. Rome. Robust gene selection from microarray data with a novel Markov boundary learning method: Application to diabetes analysis. European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Verona, Italy, LNAI 5590-Springer Verlag, pages 724-735, 2009.
G. Thibault, A. Aussem and S. Bonnevay. Incremental Bayesian network learning for scalable feature selection. International Symposium on Intelligent Data Analysis (IDA), LNCS 5772, Springer-Verlag, Lyon, France, pages 202-212, 2009.
S. Rodrigues de Morais and A. Aussem. Exploiting data missingness in Bayesian network modeling. International Symposium on Intelligent Data Analysis (IDA), LNCS 5772, Springer-Verlag, Lyon, France, pages 35-46, 2009.
F. Bellal, K. Benabdeslem, A. Aussem, M. Corbex. SOM based clustering with instance-level constraints. European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pages 313-318, 2008.
Z. Hamou Mamar, P. Chainais and A. Aussem. Combining learning methods and time-scale analysis for defect diagnosis of a tramway guiding system. Mediterranean Conference on Control and Automation (MED), Congress Centre, Ajaccio, 2008.
S. R. de Morais, A. Aussem and M. Corbex. Handling almost-deterministic relationships in constraint-based Bayesian network discovery: Application to cancer risk factor identification. European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pages 101-106, 2008.
S. R. de Morais and A. Aussem. A novel scalable and data efficient feature subset selection algorithm. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Antwerp, Belgium, pages 298-312, 2008.
S. R. de Morais and A. Aussem. A novel scalable and correct Markov boundary learning algorithm under faithfulness condition. European Workshop on Probabilistic Graphical Models (PGM), Denmark, 2008.
A. Aussem and S. R. de Morais. A conservative feature subset selection algorithm with missing data. IEEE International Conference on Data Mining (ICDM), Pisa, Italy, pages 725-730, 2008.
A. Aussem, S. R. de Morais and M. Corbex. Nasopharyngeal carcinoma data analysis with a novel bayesian network skeleton learning algorithm. International Conference on Artificial Intelligence in Medicine (AIME), LNAI 4594, Springer-Verlag, Amsterdam, The Netherlands, pages 326-330, 2007.
Aussem and P. Chainais. Modelling switching dynamics using prediction experts operating on distinct wavelet scales. European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 26-28, 2006, Proceedings, pages 185-190, 2006.
Mahul and A. Aussem. Learning with monotonicity requirements for optimal routing with end-to-end quality of service constraints. European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 26-28, 2006, Proceedings, pages 455-460, 2006.
Z. Hamou Mamar, P. Chainais, and A. Aussem. Probabilistic classifiers and time-scale representations : application to the monitoring of a tramway guiding system. European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 26-28, 2006, Proceedings, pages 659-664, 2006.
Mahul A., Aussem A. Distributed Neural Networks for Quality of Service Estimation in Communication Networks. International Journal of Computational Intelligence and Applications pages 297-308, 2003.
Aussem A. Gradient decay analysis and stability conditions for dynamical recurrent neural networks. Neural Computation, pages 1907-1927, 2002.
Hill D., Coquillard P., Aussem A., De Vaugelas J., Thibault T., Meinesz A. Modeling the ultimate seeweed expansion. Simulation 76(2), 2001.
Aussem A., Murtagh F. Web traffic demand forecasting using a wavelet-based multiscale decomposition. International Journal of Intelligent Systems 16(2), 2001.
A. Aussem and C. Boutevin. Segmentation of Switching Dynamics with a Hidden Markov Model of Neural Prediction Experts. European Symposium on Artificial Neural Networks, ESANN'2001, Bruges, Belgium, 25-27 April 2001, pp. 251-256.
Aussem A., Hill D. Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. Neurocomputing (2000) 71-78.
Murtagh F., Zheng G., Campbell J.G., Aussem A. Neural network modelling for environmental prediction. Neurocomputing 30 (2000) 65-70.
A. Aussem. Sufficient conditions for error back flow convergence in Dynamical Recurrent Neural Networks. IEEE-INNS-ENNS International Joint Conference on Neural Networks}, Vol. 4, Como, Italy 24-27 July 2000, pp. 577-582.
A. Aussem, A. Mahul and Raymond Marie. Queueing network modelling with distributed neural networks for service quality estimation in B-ISDN networks. IEEE-INNS-ENNS International Joint Conference on Neural Networks, Vol. 5, Como, Italy 24-27 July 2000, pp. 392-397.
Aussem A., Hill D. Wedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean sea Ecological Modelling 120 (1999) 225-236.
Aussem A. Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. Neurocomputing (1999) 207-232.
A. Aussem, S??bastien Rouxel and Raymond Marie. Neural-based queueing system modelling for service quality estimation in B-ISDN networks. International Conference on Artificial Neural Networks ICANN'99, Edinburgh, Scotland, 1999, pp. 970-975.
Aussem A., Campbell J., Murtagh F. Wavelet-based feature extraction and decomposition strategies for financial forecasting. Journal of Computational Intelligence in Finance 6, 2 (1998) 5-12.
Aussem A., Murtagh F. A neuro-wavelet strategy for Web traffic forecasting. Research in Official Statistics 1, 1 (1998) 65-87.
F. Murtagh and A. Aussem. Using the wavelet transform for multivariate data analysis and time series forecasting. Data Science, Classification and Related Methods, in: C. Hayashi, H.H. Bock, K. Yajima, Y. Tanaka, N. Ohsumi and Y. Baba (Eds.), Springer-Verlag, pp. 617-624, 1998.
F. Murtagh, J. Cambpell, G. Zheng, A. Aussem, M. Ouberdous, E. Demirov, W. Eifler and M. Cr??pon. Data imputation and nowcasting using clustering and connectionist modeling. Compstat'98, International Conference on Computational Statistics, in R. Payne and P. Green (Eds.), Springer, Berlin, pp. 401-406, 1998.
A. Aussem. Nonlinear modeling of chaotic processes with dynamical recurrent neural networks. Neural Networks and Their Applications NEURAP'98, Marseille, France, pp. 425-433, 1998.
F. Murtagh, A. Aussem, J.-L. Starck, J.G. Cambpell, and G. Zheng. Wavelet-based decomposition methods for feature extraction and forecasting. OESI-IMVIP'98, Optical Engineering Society of Ireland and Irish Machine Vision and Image Processing Joint Conference, D. Vernon, Ed., NUI Maynooth, pp. 55-67, 1998.
Murtagh F., Aussem A., Starck J.-L. Multiscale Data Analysis -- Information Fusion and Constant-Time Clustering. Vistas in Astronomy 41 (1997) 359-364.
Aussem A., Murtagh F. Combining Neural Network Forecasts on Wavelet-transformed Time Series. Connexion Science 9, 1 (1997) 113-122.
Aussem A., Murtagh F., Sarazin M. Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network. Neurocomputing 13 (1996) 359-373.
F. Murtagh, A. Aussem et O. Kardaun. The wavelet transform in multivariate data analysis. COMPSTAT'96, A. Prat, Ed., Physica-Verlag, Heidelberg, pp. 397-402, 1996.
F. Murtagh, A. Aussem. New problems and approaches related to large databases in astronomy. Statistical Challenges in Modern Astronomy II, G.J. Babu and E.D. Feigelson, eds., Springer-Verlag, Penn State Univ., pp. 123-133, June 1996.
Murtagh F., Aussem A., Sarazin M. Nowcasting astronomical seeing: towards an operational approach. Publications of the Astronomical Society of the Pacific (1995) 702-707.
Aussem A., Murtagh D.P., Sarazin M. Dynamical recurrent neural networks -- towards environmental time series prediction. International Journal on Neural Systems 6, 2 (1995) 145-170.
Aussem A., Murtagh F., Sarazin M. Dynamical recurrent neural networks and pattern recognition methods for time series prediction: Application to seeing and temperature forecasting in the context of ESO's VLT Astronomical Weather Station. Vistas in Astronomy 38 (1994) 357--374.
Aussem. Call admission control in ATM networks with the random neural network. IEEE International Conference on Neural Networks 4, pp. 2482-2487, 1994.
A. Aussem. Le Calcul du Gradient d'Erreur dans les Réseaux de Neurones :
Applications aux Télécom et aux Sciences Environnementales.
LIMOS UMR 6158 CNRS, 2002.
A. Aussem. Théorie et applications des réseaux de neurones récurrents et dynamiques, a la prédiction, a la modélisation et au controle adaptatif des processus dynamiques. University of Paris 5, 1995.
