Publications

1. Olteanu, M., Rossi, F., & Yger, F. (2023). Meta-survey on outlier and anomaly detection. Neurocomputing. Vol. 555, p. 126634, https://doi.org/10.1016/j.neucom.2023.126634. 

2. Laroche, C., Olteanu, M., & Rossi, F. (2023). Pesticide concentration monitoring: Investigating spatio-temporal patterns in left censored data. Environmetrics, Vol. 34(2), e2756. https://doi.org/10.1002/env.2756

3. de Bézenac, C., Clark, W.A.V., Olteanu, M., & Randon-Furling, J. (2022), Measuring and Visualising Patterns of Ethnic Concentration: The Role of Distortion Coefficients. Geographical Analysis, Vol. 54, p.173-196. https://doi.org/10.1111/gean.12271

4. Olteanu, M., de Bezenac, C., Clark, W. et al. (2020) Revealing Multiscale Segregation Effects from Fine-Scale Data: A Case Study of Two Communities in Paris. Spatial Demography, Vol. 8, p.255–267. https://doi.org/10.1007/s40980-020-00065-4

5. Cottrell, M., Hazan, A., Olteanu, M., & Randon-Furling, J. (2020) Multidimensional urban segregation - toward a neural network measure. Neural Computing and Applications. Vol. 32 (24), p. 18179-18191. https://doi.org/10.1007/s00521-019-04199-5

6. Olteanu, M., Randon-Furling, J., & Clark, W. (2019). Segregation through the multiscalar lens, PNAS, Vol. 116 (25), p. 12250-12254.

7. Randon-Furling, J., Olteanu, M., & Lucquiaud, A. (2018). From urban segregation to spatial structure detection. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/2399808318797129

8. Cottrell, M., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. (2018). Self-organising maps, theory and applications. Revista de Investigacion Operacional, Vol. 39 (1), p.1-22. Preprint

9. Alerini, J., Olteanu, M.,  & Ridgway, J. (2017). Markov and the Duchy of Savoy: segmenting a century with regime-switching models. Journal de la SFdS, Vol. 158 (2), p. 89-117. Preprint

10. Mariette, J., Olteanu, M., & Villa-Vialaneix, N. (2017). Efficient interpretable variants of online SOM for large dissmilarity data. Neurocomputing, Vol. 225, p. 31-48. 

11. Olteanu, M., & Villa-Vialaneix, N. (2015). Using SOMbrero for clustering and visualizing graphs. Journal de la SFdS, Vol. 156(3), p. 95-119.

12. Olteanu, M., & Villa-Vialaneix, N. (2015). Online relational and multiple relational SOM. Neurocomputing, Vol. 147, p. 15-30.

13. Olteanu, M., Nicolas, V., Schaeffer, B., Denys, C., Missoup, A.-D., Kennis, J., & Laredo, C. (2013). Nonlinear projection methods for visualizing Barcode data and application on two data sets. Molecular Ecology Resources, 2013 online, 15 p. https://doi.org/10.1111/1755-0998.12047

14. Cottrell, M., Olteanu, M., Rossi., F., Rynkiewicz, J., & Villa-Vialaneix, N. (2012). Neural networks for complex data. KI - Kunstliche Intelligenz, Vol. 26, p.373-380

15. Olteanu, M. & Rynkiewicz, J. (2012). Asymptotic properties of autoregressive regime-switching models. ESAIM P&S. Vol. 16, p. 25-47.

16. Olteanu, M. & Rynkiewicz, J. (2011). Asymptotic properties of mixture-of-experts models. Neurocomputing. Vol. 74(9), p. 1444-1449.

17. Austerlitz, F., Bleakley, K., David, O., Laredo, C., Leblois, R., Olteanu, M., Schaeffer, B., & Veuille, M. (2009). DNA barcode analysis: comparing phylogenetic and statistical classification methods. BMC Bioinformatics. Vol. 10(suppl 14).

18. Olteanu, M. & Rynkiewicz, J. (2008). Estimating the number of components in a mixture of multilayer perceptrons. Neurocomputing. Vol. 71(7-9), p. 1321-1329.

19. Boyer-Xambeu, M.T., Deleplace, G., Gaubert, P., Gillard, L., & Olteanu, M. (2007). The periodization of the international bimetallism: 1821-1873. Revista Investigacion Operacional. Vol. 28(2), p. 143-156.

20. Olteanu, M. (2006). A descriptive method to evaluate the number of regimes in a switching autoregressive model. Neural Networks. Vol. 19, p. 963-972.

21. Maillet, B., Olteanu, M., & Rynkiewicz, J. (2004). Caractérisation des crises financières à l'aide de modèles hybrides (HMC-MLP). Revue d'économie politique. Vol. 4, p. 489-506

Peer-reviewed proceedings in international conferences

1. Olteanu, M., Rossi, F., & Yger, F. (2022). Challenges in anomaly and change-point detection. Proceedings of ESANN 2022 (European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning). p.277-286. https://doi.org/10.14428/esann/2022.ES2022-6 

2. Chavent, M., Cottrell, M., Lacaille, J., Mourer, A., Olteanu, M. (2022). Sparse Weighted K-Means for Groups of Mixed-Type Variables. In: Faigl, J., Olteanu, M., Drchal, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-15444-7_1

3. Chavent, M., Mourer, A., Lacaille, J., & Olteanu, M. (2021) Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability? Proceedings of ESANN 2021 (European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning). p.569-574. https://doi.org/10.14428/esann/2021.ES2021-155

4. Chavent, M., Mourer, A., Lacaille, J., & Olteanu, M. (2020) Sparse K-means for mixed data via group-sparse clustering. Proceedings of ESANN 2020 (European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning). p. 235-240.  Manuscript.

5. Cottrell, M., Faure, C., Lacaille, J., & Olteanu, M. (2019) Anomaly detection for bivariate signals, Proceedings of IWANN 2019.

6. Cottrell, M., Faure, C., Lacaille, J., & Olteanu, M. (2019) Detection of abnormal flights using fickle instances in SOM maps, Proceedings of WSOM+ 2019.

7. Olteanu, M., & Lamirel, J-C. (2019) When clustering the multiscalar fingerprint of the city reveals its segregation patterns, Proceedings of WSOM+ 2019.

8. Olteanu, M., Randon-Furling, J., & Clark, W. (2019). Spatial analysis in high resolution geo-data, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019).

9. Alerini, J., Cottrell, M., & Olteanu, M. (2017). Hidden Markov models for time series of continuous proportions with excess zeros. In I. Rojas, G. Joya and A. Catala (dir.), Advances in Computational Intelligence. 14th International Work-Conference on Artificial Neural Networks, IWANN 2017. Proceedings, Part II, New York, Springer. p. 198-209. 

10. Cottrell, M., Olteanu, M., Randon-Furling, J., & Hazan, A. (2017). Multidimensional urban segregation: an exploratory case study. 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+). IEEE Xplore. 

11. Faure, C., Olteanu, M., Bardet, J-M., & Lacaille, J. (2017). Using self-organizing maps for clustering and labelling aircraft engine data phases. 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+). IEEE Xplore. 

12. Mariette, J., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. (2017). Accelerating stochastic kernel SOM. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017). p. 269-274.  

13. Bardet, J.-M., Faure, C., Lacaille, J., & Olteanu, M. (2016). Comparison of three algorithms for parametric change-point detection. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016). p. 89-94.  

14. Olteanu, M., & Villa-Vialaneix, N. (2016). Sparse online self-organizing maps for large relational data. In E.  Merényi, M. J. Mendenhall, & O.D.P. (Eds.), Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016). Vol. 428, p. 27-37. Springer International Publishing. 

15. Bourgeois, N., Cottrell, M., Lamassé, S., & Olteanu, M. (2015). Search for meaning through the study of co-occurrences in texts. Advances in Computational Intelligence (Proceedings of IWANN 2015), Lecture Notes in Computer Science. p. 578-591. Springer. 

16. Bendhaiba, L., Boelaert, J., Olteanu, M., & Villa-Vialaneix, N. (2014). SOMbrero: an R package for numeric and non-numeric self-organizing maps. Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014). p. 219-228. Berlin Springer Verlag. 

17. Mariette, J., Olteanu, M., & Villa-Vialaneix, N. (2014). Bagged kernel SOM. Advances in Self-organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014). p. 45-54. Berlin Springer Verlag. 

18. Cierco-Ayrolles, C., Olteanu, M., & Villa-Vialaneix, N. (2013). Multiple kernel self-organizing maps. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013). p. 83-88.  

19. Massoni, S., Olteanu, M., & Villa-Vialaneix, N. (2013). Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study. Advances in Computational Intelligence (Proceedings of IWANN 2015), Lecture Notes in Computer Science. p. 69-79. Springer. 

20. Cottrell, M., Olteanu, M., & Villa-Vialaneix, N. (2012). Online relational SOM for dissimilarity data. Advances in Self-organizing Maps (Proceedings of WSOM 2012). p. 13-22. Springer. 

21. Olteanu, M., & Ridgway, J. (2012). Hidden Markov models for time series of counts with excess zeros. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012). p. 133-138.  

22. Olteanu, M., & Rynkiewicz, J. (2010). Asymptotic properties of mixture-of-experts models. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2010). p. 207-212.

23. Massoni, S., Olteanu, M., & Rousset, P. (2010). Career-path analysis using drifting Markov models (DMM) and self-organizing maps. Proceedings of MASHS 2010 (Modelling and leArning in Social and Human Sciences). p. 171-179. 

24. Massoni, S., Olteanu, M., & Rousset, P. (2009). Career-path analysis using optimal matching and self-organizing maps. Advances in Self-Organizing Maps: Volume 5629 of Lecture Notes in Computer Science, Proceedings of WSOM 2017. p. 154-162.  

25. Bouveyron, C., Girard, S., & Olteanu, M. (2009). Supervised classification of categorical data with uncertain labels for DNA barcoding. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2009). p. 22-34.

26. Massoni, S., Olteanu, M., & Rousset, P. (2009). Analyse des trajectoires d'insertion professionnelle avec un algorithme de Kohonen pour données catégorielles. Proceedings of MASHS 2009 (Modelling and leArning in Social and Human Sciences). 

27. Olteanu, M. (2008). Revisiting linear and nonlinear methodologies for time series prediction: application to ESTSP'08 competition data. Proceedings of European Symposium on Time Series Prediction (ESTSP 2008). p. 139-148.

28. Olteanu, M., & Rynkiewicz, J. (2007). Estimating the number of components of a mixture autoregressive model. Proceedings of European Symposium on Time Series Prediction (ESTSP 2007). p. 143-154.

29. Olteanu, M., & Rynkiewicz, J. (2007). Estimating the number of components in a mixture of multilayer perceptrons. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2007). p. 403-408.

30. Olteanu, M. (2005) A descriptive method to evaluate the number of regimes in a switching autoregressive model. Proceedings of WSOM 2005 (Workshop on Self-Organizing Maps). p. 259-266.

31. Maillet, B., Olteanu, M., & Rynkiewicz, J. (2004). Nonlinear analysis of shocks when financial markets are subject to changes in regime. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2004). p. 87-92.

1. Mariette, J., Olteanu, M., & Vialaneix, N. (2021). Kernel and dissimilarity methods for exploratory analysis in a social context. In A. Daouia & A. Ruiz-Gazen (Eds.), Advances in Contemporary Statistics and Econometrics. Festschrift in Honour of Christine Thomas-Agnan. p. 669-690. Springer, Cham. Publisher link.

2. Nahassia, L., Gravier, J., Michelet, D., Verdier, N., Olteanu, M. (2020) Processus. In Sanders, L. (dir), Bretagnolle, A. (ed), Brun, P. (ed), Ozouf-Marignier,  M.-V. (ed), Verdier, N. (ed). Le temps long du peuplement. Concepts et mots-clés. Presses universitaires de Tours. p.89-106.

3. Gravier, J., Nahassia, L., Verdier, N., Olteanu, M.,  Michelet, D. (2020) Trajectoire. In Sanders, L. (dir), Bretagnolle, A. (ed), Brun, P. (ed), Ozouf-Marignier,  M.-V. (ed), Verdier, N. (ed). Le temps long du peuplement. Concepts et mots-clés. Presses universitaires de Tours. p.89-106.

4. Olteanu, M., & Alerini, J. (2019). Quelques réflexions sur la périodisation en histoire. In St. Lamassé (Ed.) Dans les dédales du web: Historiens en territoires numériques, Editions de la Sorbonne. 

1. Faigl, J., Olteanu, M., & Drchal, J. (2022) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, Cham.  

2. Lamirel, J.-C., Cottrell, M., Olteanu, M., & Lévy, B. (2020) Editorial of the Special Issue on WSOM+ 2017, Neural Computing and Applications, Springer Verlag, Vol. 32, p.17973-17975. https://doi.org/10.1007/s00521-020-05481-7  

3. Cottrell, M., Olteanu, M., Rouchier, J., & Villa-Vialanei, N. (2013) Editorial of the Special Issue RNTI - MASHS 2011/2012 : Modèles et Apprentissages en Sciences Humaines et Sociales. Revue Des Nouvelles Technologies De l’Information, SHS-1, p.97–110.

1. Olteanu, M., & Rossi, F. (2021) Des données massivement biaisées ? In Finance et Gestion DFCG, La revue des dirigeants financiers. 

1. Olteanu, M. (2019). Some reflections about time, durations and transitions when mining complex data. A statistical perspective. HDR manuscript. 

2. Olteanu, M. (2006). Modèles à changements de régime, applications aux données financières. PhD manuscript.