Dynamical recurrent neural networks – towards environmental time series prediction. - Alex Aussem; Fionn Murtagh; Marc Sarazin (Int. J. Neural Syst., 1995)

Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network. - Alex Aussem; Fionn Murtagh; Marc Sarazin (Neurocomputing, 1996)

Combining Neural Network Forecasts on Wavelet-transformed Time Series. - Alex Aussem; Fionn Murtagh (Connect. Sci., 1997)

Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling. - Fionn Murtagh; G. Zheng; Jonathan G. Campbell; Alex Aussem; M. Ouberdous; E. Demirov; Walter Eifler; M. Crépon (COMPSTAT, 1998)

Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. - Alex Aussem (Neurocomputing, 1999)

Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks. - Alex Aussem (IJCNN (4), 2000)

Queuing Network Modeling with Distributed Neural Networks for Service Quality Estimation in B-ISDN Network. - Alex Aussem; Antoine Mahul; Raymond Marie (IJCNN (5), 2000)

Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. - Alex Aussem; David R. C. Hill (Neurocomputing, 2000)

Neural network modelling for environmental prediction. - Fionn Murtagh; G. Zheng; Jonathan G. Campbell; Alex Aussem (Neurocomputing, 2000)

Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts. - Alex Aussem; C. Boutevin (ESANN, 2001)

Web traffic demand forecasting using wavelet-based multiscale decomposition. - Alex Aussem; Fionn Murtagh (Int. J. Intell. Syst., 2001)

Modeling the Ultimate Seaweed Expansion. - David R. C. Hill; Patrick Coquillard; Alex Aussem; Jean de Vaugelas; Thierry Thibaut; Alexandre Meinesz (Simul., 2001)

Le Calcul du Gradient d'Erreur dans les Réseaux de Neurones : Applications aux Telecom et aux Sciences Environnementales. - Alexandre Aussem (2002)

e-functional dependency inference: application to DNA microarray expression data. - Alex Aussem; Jean-Marc Petit (BDA, 2002)

Sufficient Conditions for Error Backflow Convergence in Dynamical Recurrent Neural Networks. - Alex Aussem (Neural Comput., 2002)

Closed Loop Stability of FIR-Recurrent Neural Networks. - Alex Aussem (ICANN, 2003)

Distributed Neural Networks for Quality of Service Estimation in Communication Networks. - Antoine Mahul; Alex Aussem (Int. J. Comput. Intell. Appl., 2003)

Modelling switching dynamics using prediction experts operating on distinct wavelet scales. - Alexandre Aussem; Pierre Chainais (ESANN, 2006)

Learning with monotonicity requirements for optimal routing with end-to-end quality of service constraints. - Antoine Mahul; Alexandre Aussem (ESANN, 2006)

Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system. - Zahra Hamou Mamar; Pierre Chainais; Alexandre Aussem (ESANN, 2006)

Apprentissage de la structure des réseaux bayésiens à partir des motifs fréquents corrélés : application à l'identification des facteurs environnementaux du cancer du Nasopharynx. - Alexandre Aussem; Zahra Kebaili; Marilys Corbex; Fabien De Marchi (EGC, 2006)

Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm. - Alex Aussem; Sergio Rodrigues de Morais; Marilys Corbex (AIME, 2007)

Application des réseaux bayésiens à l'analyse des facteurs impliqués dans le cancer du Nasopharynx. - Alexandre Aussem; Sergio Rodrigues de Morais; Marilys Corbex (EGC, 2007)

Approche connexionniste pour l'extraction de profils cas-témoins du cancer du Nasopharynx à partir des données issues d'une étude épidémiologique. - Khalid Benabdeslem; Mustapha Lebbah; Alexandre Aussem; Marilys Corbex (EGC, 2007)

A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques. - Zahra Kebaili; Alex Aussem (ICDIM, 2007)

Learning Bayesian network structures by estimation of distribution algorithms: An experimental analysis. - Grégory Thibault; Stéphane Bonnevay; Alexandre Aussem (ICDIM, 2007)

Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm. - Alex Aussem; Sergio Rodrigues de Morais; Marilys Corbex (RIVF, 2007)

SOM based clustering with instance-level constraints. - Fazia Bellal; Khalid Benabdeslem; Alexandre Aussem (ESANN, 2008)

Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification. - Sergio Rodrigues de Morais; Alexandre Aussem; Marilys Corbex (ESANN, 2008)

Intégration de contraintes dans les cartes auto-organisatrices. - Anouar BenaHassena; Khalid Benabdeslem; Fazia Bellal; Alexandre Aussem; Bruno Canitia (EGC, 2008)

A Novel Scalable and Data Efficient Feature Subset Selection Algorithm. - Sergio Rodrigues de Morais; Alex Aussem (ECML/PKDD (2), 2008)

Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis. - Alex Aussem; Sergio Rodrigues de Morais; Florence Perraud; Sophie Rome (ECSQARU, 2009)

Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods. - Alex Aussem; Sergio Rodrigues de Morais; Marilys Corbex; Joël Favrel (GbRPR, 2009)

Exploiting Data Missingness in Bayesian Network Modeling. - Sergio Rodrigues de Morais; Alex Aussem (IDA, 2009)

Incremental Bayesian Network Learning for Scalable Feature Selection. - Grégory Thibault; Alex Aussem; Stéphane Bonnevay (IDA, 2009)

Une approche de co-classification automatique à base des cartes topologiques. - Kais Allab; Khalid Benabdeslem; Alexandre Aussem (AAFD, 2010)

Feature Selection for Unsupervised Learning Using Random Cluster Ensembles. - Haytham Elghazel; Alex Aussem (ICDM, 2010)

An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery. - Sergio Rodrigues de Morais; Alex Aussem (ECML/PKDD (3), 2010)

Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks. - Alex Aussem; André Tchernof; Sergio Rodrigues de Morais; Sophie Rome (BMC Bioinform., 2010)

Bayesian networks. - Alex Aussem (Neurocomputing, 2010)

A conservative feature subset selection algorithm with missing data. - Alex Aussem; Sergio Rodrigues de Morais (Neurocomputing, 2010)

A novel Markov boundary based feature subset selection algorithm. - Sergio Rodrigues de Morais; Alex Aussem (Neurocomputing, 2010)

Semi-supervised Feature Importance Evaluation with Ensemble Learning. - Hasna Barkia; Haytham Elghazel; Alex Aussem (ICDM, 2011)

Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests. - Haytham Elghazel; Alex Aussem; Florence Perraud (Ensembles in Machine Learning Applications, 2011)

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. - Maxime Gasse; Alex Aussem; Haytham Elghazel (ECML/PKDD (1), 2012)

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks. - Alex Aussem; Sergio Rodrigues de Morais; Marilys Corbex (Artif. Intell. Medicine, 2012)

A semi-supervised feature ranking method with ensemble learning. - Fazia Bellal; Haytham Elghazel; Alex Aussem (Pattern Recognit. Lett., 2012)

Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours. - Emmanuel Prestat; Sergio Rodrigues de Morais; Julie A. Vendrell; Aurélie Thollet; Christian Gautier; Pascale A. Cohen; Alex Aussem (Comput. Biol. Medicine, 2013)

A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm. - Ouadie Gharroudi; Haytham Elghazel; Alex Aussem (Canadian AI, 2014)

Analysis of risk factors of hip fracture with causal Bayesian networks. - Alex Aussem; Pascal Caillet; Zara Klemm; Maxime Gasse; Anne-Marie Schott; Michel Ducher (IWBBIO, 2014)

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. - Maxime Gasse; Alex Aussem; Haytham Elghazel (Expert Syst. Appl., 2014)

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. - Maxime Gasse; Alexandre Aussem; Haytham Elghazel (ICML, 2015)

Calibrated k-labelsets for Ensemble Multi-label Classification. - Ouadie Gharroudi; Haytham Elghazel; Alex Aussem (ICONIP (1), 2015)

Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation. - Van-Tinh Tran; Alex Aussem (ICONIP (3), 2015)

Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods. - Ouadie Gharroudi; Haytham Elghazel; Alex Aussem (ICTAI, 2015)

A Practical Approach to Reduce the Learning Bias Under Covariate Shift. - Van-Tinh Tran; Alex Aussem (ECML/PKDD (2), 2015)

Unsupervised feature selection with ensemble learning. - Haytham Elghazel; Alex Aussem (Mach. Learn., 2015)

A Semi-Supervised Ensemble Approach for Multi-label Learning. - Ouadie Gharroudi; Haytham Elghazel; Alexandre Aussem (ICDM Workshops, 2016)

Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach. - Anil Narassiguin; Haytham Elghazel; Alex Aussem (ICDM Workshops, 2016)

Identifying the irreducible disjoint factors of a multivariate probability distribution. - Maxime Gasse; Alex Aussem (Probabilistic Graphical Models, 2016)

F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. - Maxime Gasse; Alex Aussem (ECML/PKDD (1), 2016)

Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. - Haytham Elghazel; Alex Aussem; Ouadie Gharroudi; Wafa Saadaoui (Expert Syst. Appl., 2016)

An extensive empirical comparison of ensemble learning methods for binary classification. - Anil Narassiguin; Mohamed Bibimoune; Haytham Elghazel; Alex Aussem (Pattern Anal. Appl., 2016)

Reducing variance due to importance weighting in covariate shift bias correction. - Van-Tinh Tran; Alex Aussem (ESANN, 2017)

Dynamic Ensemble Selection with Probabilistic Classifier Chains. - Anil Narassiguin; Haytham Elghazel; Alex Aussem (ECML/PKDD (1), 2017)

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. - Denis Lecoeuche; Alex Aussem; Maxime Gasse (INNS Conference on Big Data, 2018)

Recurrent Neural Network Approach for Table Field Extraction in Business Documents. - Clément Sage; Alexandre Aussem; Haytham Elghazel; Véronique Eglin; Jérémy Espinas (ICDAR, 2019)

Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots. - Jean-Baptiste Aujogue; Alex Aussem (IJCNN, 2019)

End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks. - Clément Sage; Alex Aussem; Véronique Eglin; Haytham Elghazel; Jérémy Espinas (SPNLP@EMNLP, 2020)

Building bagging on critical instances. - Li Guo 005; Samia Boukir; Alexandre Aussem (Expert Syst. J. Knowl. Eng., 2020)

Data-Efficient Information Extraction from Documents with Pre-trained Language Models. - Clément Sage; Thibault Douzon; Alex Aussem; Véronique Eglin; Haytham Elghazel; Stefan Duffner; Christophe Garcia; Jérémy Espinas (ICDAR Workshops (2), 2021)

Neural Embedded Dirichlet Processes for Topic Modeling. - Miguel Palencia-Olivar; Stéphane Bonnevay; Alexandre Aussem; Bruno Canitia (MDAI, 2021)

Processus de Dirichlet profonds pour le topic modeling. - Miguel Palencia-Olivar; Stéphane Bonnevay; Alexandre Aussem; Bruno Canitia (EGC, 2022)

Nonparametric neural topic modeling for customer insight extraction about the tire industry. - Miguel Palencia-Olivar; Stéphane Bonnevay; Alexandre Aussem; Bruno Canitia (IJCNN, 2022)

Répondre aux requêtes des étudiants avec un agent conversationnel à mémoire supervisée. - Florian Baud; Alexandre Aussem (EGC, 2023)

BERTEPro : Une nouvelle approche de représentation sémantique dans le domaine de l'éducation et de la formation professionnelle. - Guillaume Lefebvre; Haytham Elghazel; Théodore Guillet; Alexandre Aussem; Matthieu Sonnati (EGC, 2023)

Topic modeling neuronal non-paramétrique pour l'extraction d'insight client : une application à l'industrie du pneumatique. - Miguel Palencia-Olivar; Stéphane Bonnevay; Alexandre Aussem; Bruno Canitia (EGC, 2023)

Answering Student Queries with a Supervised Memory Conversational Agent. - Florian Baud; Alex Aussem (FLAIRS, 2023)

Non-Parametric Memory Guidance for Multi-Document Summarization. - Florian Baud; Alex Aussem (RANLP, 2023)

BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain. - Guillaume Lefebvre; Haytham Elghazel; Théodore Guillet; Alexandre Aussem; Matthieu Sonnati (SAC, 2023)

Résumé automatique multi-documents guidé par une base de résumés similaires. - Florian Baud; Alexandre Aussem (CORIA-TALN (4), 2023)

A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification. - Guillaume Lefebvre; Haytham Elghazel; Théodore Guillet; Alexandre Aussem; Matthieu Sonnati (Data Knowl. Eng., 2024)

LineRetriever: Planning-Aware Observation Reduction for Web Agents. - Imene Kerboua; Sahar Omidi Shayegan; Megh Thakkar; Xing Han Lù; Massimo Caccia; Véronique Eglin; Alexandre Aussem; Jérémy Espinas; Alexandre Lacoste (CoRR, 2025)