Publications

• Machine learning and data mining
• Bioinformatics - Biomedical data analysis
  
  
• Constraint programming (2004-2007)
• Pedagogy (2005-2006)
• Speech and language processing (1989-2005)

Citation Index:

Machine learning and data mining

1. V. Hamer and P. Dupont,
   Robust Selection Stability Estimation in Correlated Spaces,
   Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD),
   Lecture Notes in Computer Science, Vol. 12977, pp. 446-461, Springer International Publishing, 2021.

2. V. Hamer and P. Dupont,
   An Importance Weighted Feature Selection Stability Measure,
   Journal of Machine Learning Research,
   Vol. 22, 116, pp. 1−57, 2021.

3. V. Hamer and P. Dupont,
   Joint optimization of predictive performance and selection stability,
   Proc. of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), pp. 381-386
   Online Event, October 2-4, 2020.

4. V. Branders, G. Derval, P. Schaus, P. Dupont,
   Mining a maximum weighted set of disjoint submatrices,
   Proc. of the International Conference on Discovery Science, pp. 18-28
   Springer, Cham, October, 2019.

5. V. Hamer and P. Dupont,
   Explicit Control of Feature Relevance and Selection Stability Through Pareto Optimality,
   Proc. of the Interactive Adaptive Learning (IAL2019) Workshop, at ECML-PKDD,
   CEUR Workshop Proceedings - Vol. 2444, no.1, pp. 64-79,
   Würzburg, Germany, September, 2019.

6. G. Derval, V. Branders, P. Dupont, P. Schaus, The maximum weighted submatrix coverage problem: a CP approach,
   Proc. of the 16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR'19), Thessaloniki, Greece,
   Lecture Notes in Computer Science, No. 11494, Springer Nature Switzerland AG 2019, pp. 258-274, June 2019.

7. V. Hamer and P. Dupont,
   Learning Computationally Efficient Metrics for Large Scale Person Identification,
   Proc. of the Benelearn 2018 conference,
   `s-Hertogenbosch, The Netherlands, Nov. 8-9, 2018.

8. V. Branders, P. Schaus and P. Dupont,
   Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum,
   [Preprint],
   Lecture Notes in Artificial Intelligence, No. 10785, Springer Nature, pp. 65-79, January 1, 2018.

9. V. Branders, P. Schaus and P. Dupont,
   Mining a Sub-Matrix of Maximal Sum,
   The 6th International Workshop on New Frontiers in Mining Complex Patterns,
   Skopje, Macedonia, Sep 22, 2017.

10. J. Paul, R. D'Ambrosio and P. Dupont,
    Kernel Methods for Heterogeneous Feature Selection, [Preprint],
    Neurocomputing,
    Vol. 169, pp. 187-195, 2 December 2015.

11. S. Branders, B. Frenay and P. Dupont,
    Survival Analysis with Cox Regression and Random Non-linear Projections,
    Proc. of the 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15),
    Bruges, Belgium, April 22-24, 2015.

12. J. Paul and P. Dupont,
    Inferring Statistically Significant Features from Random Forests, [Preprint],
    Neurocomputing,
    Vol. 150, Part B, pp. 471-480, February 2015.

13. R. De Visscher, V. Delouille, P. Dupont and C.-A. Deledalle,
    Supervised Classification of Solar Features using Prior Information,
    Journal of Space Weather and Space Climate,
    DOI:10.1051/swsc/2015033, Vol. 5, A34, 2015.

14. S. Branders, R. d'Ambrosio and P.Dupont
    A mixture Cox-Logistic model for feature selection from survival and classification data ,
    arXiv:1502.01493 [stat.ML],
    February, 2015.

15. S. Branders, R. d'Ambrosio and P.Dupont
    The Coxlogit model: feature selection from survival and classification data [Preprint],
    Proc. of the IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making,
    Orlando, Florida, Dec 9-12, pp. 137-143, 2014.

16. J. Paul and P. Dupont,
    Statistically interpretable importance indices for Random Forests,
    Proc. of the 23rd Belgian-Dutch Conference on Machine Learning (BENELEARN'14),
    Brussels, Belgium, June 6, 2014.

17. J. Paul and P. Dupont,
    Kernel methods for mixed feature selection,
    Proc. of the 22th European Symposium on Artificial Neural Networks (ESANN'14),
    Bruges, Belgium, April 23-25, pp. 301-306, 2014.

18. D. Hernández-Lobato, J.M. Hernández-Lobato and P. Dupont,
    Generalized Spike Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation,
    Journal of Machine Learning Research,
    Vol. 14, pp. 1891-1945, July 2013.

19. R. Zakharov and P. Dupont,
    Stable LASSO for High-Dimensional Feature Selection through Proximal Optimization, (Poster abstract),
    International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications (ROKS 2013),
    Leuven, Belgium, July 8-10, 2013.

20. J.A. Lee, E. Renard, G. Bernard, P. Dupont, and M. Verleysen,
    Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation, [PDF],
    Neurocomputing,
    Vol. 112, pp. 92-108, July, 2013.

21. J. Paul, M. Verleysen and P. Dupont,
    Identification of Statistically Relevant Features from Random Forests ,
    ECML workshop on Solving Complex Machine Learning Problems with Ensemble Methods,
    Prague, Czech Republic, September 27, 2013.

22. E. Renard, P. Dupont and M. Verleysen,
    User control for adjusting conflicting objectives in parameter-dependent visualization of data,
    EuroVis 2013 Workshop on Visual Analytics using Multidimensional Projections,
    Leipzig, Germany, June 2013.

23. N. Walkinshaw, B. Lambeau, C. Damas, K. Bogdanov and P. Dupont,
    STAMINA: A Competition to Encourage the Development and Assessment of Software Model Inference Techniques,
    Empirical Software Engineering,
    DOI 10.1007/s10664-012-9210-3, May, 2012.

24. J. Paul, M. Verleysen and P. Dupont,
    The stability of feature selection and class prediction from ensemble tree classifiers,
    20th European Symposium on Artificial Neural Networks (ESANN'12),
    Bruges, Belgium, April 25-27, pp. 263-268, 2012.

25. D. Hernández-Lobato, J.M. Hernández-Lobato and P. Dupont,
    Robust Multi-Class Gaussian Process Classification,
    Neural Information Processing Systems conference (NIPS'11),
    Granada, Spain, December 13-15, 2011.

26. R. Zakharov and P. Dupont,
    Ensemble Logistic Regression for Feature Selection,
    Lecture Notes in Bioinformatics, No. 7036, Springer, pp. 133-144, 2011
    6th IAPR International Conference on Pattern Recognition in Bioinformatics,
    Delft, The Netherlands, November 2-4.

27. D. Hernández-Lobato, J.M. Hernández-Lobato, T. Helleputte and P. Dupont,
    Expectation Propagation for Bayesian Multi-task Feature Selection,
    Lecture Notes in Artificial Intelligence, No. 6321, Springer, pp. 522-537, 2010
    European Conference on Machine Learning (ECML/PKDD), Barcelona, Spain, September 20-24.

28. N. Walkinshaw, K. Bogdanov, C. Damas, B. Lambeau and P. Dupont,
    A Framework for the Competitive Evaluation of Model Inference Techniques,
    1st International workshop on Model Inference In Testing, Trento, July 2010.

29. T. Helleputte and P. Dupont,
    Feature Selection by Transfer Learning with Linear Regularized Models,
    Lecture Notes in Artificial Intelligence, No. 5781, Springer, pp. 533-547, 2009
    European Conference on Machine Learning (ECML/PKDD),
    Bled, Slovenia, September 7-11.

30. T. Helleputte and P. Dupont,
    Partially Supervised Feature Selection with Regularized Linear Models,
    26th International Conference on Machine Learning (ICML),
    Montreal, Canada, June 14-18, 2009.

31. J. Nariño-Mendoza, B. Donnet and P. Dupont,
    A Comparative Study of Path Performance Metrics Predictors,
    Advances in Learning for Networking, ACM Workshop in conjunction with SIGMETRICS/Performance 2009,
    Seattle, USA, June 15, 2009.

32. B. Lambeau, C. Damas and P. Dupont,
    State-merging DFA Induction Algorithms with Mandatory Merge Constraints,
    Lecture Notes in Artificial Intelligence No. 5278, Springer, pp. 139-153, 2008
    9th International Colloquium on Grammatical Inference,
    St Malo, France, September 22-24.

33. J. Callut, K. Françoisse, M. Saerens and P. Dupont,
    Semi-supervised Classification from Discriminative Random Walks,
    Lecture Notes in Artificial Intelligence No. 5211, Springer, pp. 162-177, 2008.
    European Conference on Machine Learning (ECML PKDD, Part I),
    Antwerp, Belgium, September 15-19.

34. J. Callut, K. Françoisse, M. Saerens and P. Dupont,
    Classification in Graphs using Discriminative Random Walks,
    6th International Workshop on Mining and Learning with Graphs (MLG),
    Helsinki, Finland, July 4-5, 2008.

35. J. Callut, K. Françoisse, M. Saerens and P. Dupont,
    Semi-supervised Classification in Graphs using Bounded Random Walks,
    17th Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn), pp. 67-68,
    Liège, Belgium, May 19-20, 2008.

36. P. Dupont, B. Lambeau, C. Damas and A. van Lamsweerde,
    The QSM Algorithm and its Application to Software Behavior Model Induction,
    Applied Artificial Intelligence,
    Vol. 22, Issue 1, pp. 77-115, January 2008.

37. J. Callut and P. Dupont,
    Learning Partially Observable Markov Models from First Passage Times,
    Lecture Notes in Artificial Intelligence, No. 4701, Springer, pp. 91-103, 2007
    European Conference on Machine Learning (ECML),
    Warsaw, Poland.

38. P. Dupont, J. Callut, G. Dooms, J-N. Monette and Y. Deville,
    Relevant subgraph extraction from random walks in a graph,
    Research Report UCL/FSA/INGI RR 2006-07,
    November 2006.

39. J. Callut and P. Dupont,
    Sequence Discrimination using Phase-type Distributions,
    Lecture Notes in Artificial Intelligence, No. 4212, pp. 78-89, 2006
    European Conference on Machine Learning (ECML),
    Berlin, Germany.

40. P. Dupont,
    Noisy Sequence Classification with Smoothed Markov Chains,
    Presses Universitaires de Grenoble,
    CAp 2006, Conférence d'Apprentissage, pp. 187-201, 2006
    Trégastel, France, May 22-24.

41. C. Damas, B. Lambeau, P. Dupont and A. van Lamsweerde,
    Generating Annotated Behavior Models from End-User Scenarios,
    © IEEE, IEEE Transactions on Software Engineering,
    Special Issue on Interaction and State-based Modeling,
    Vol. 31, No. 12, pp. 1056-1073, 2005.

42. J. Callut and P. Dupont,
    Inducing Hidden Markov Models to Model Long-Term Dependencies,
    Lecture Notes in Artificial Intelligence, No. 3720, Springer, pp. 513-521, 2005
    16th European Conference on Machine Learning (ECML),
    Porto, Portugal.

43. J. Callut and P. Dupont,
    Learning Hidden Markov Models to Fit Long-Term Dependencies,
    UCL-INGI Research Report RR 2005-09,
    July 2005.

44. J. Callut and P. Dupont,
    F_β support vector machines,
    IJCNN05, International Joint Conference on Neural Networks, pp. 1443-1448, 2005
    Montréal, Canada.

45. S. Vast, P. Dupont and Y. Deville,
    Automatic extraction of relevant nodes in biochemical networks,
    Atelier Apprentissage et Bioinformatique, pp. 21-31, 2005
    CAp 2005, Conférence d'Apprentissage,
    Nice, France

46. P. Dupont, F. Denis and Y. Esposito,
    Links between Probabilistic Automata and Hidden Markov Models: probability distributions, learning models and induction algorithms,
    Pattern Recognition
    Special Issue on Grammatical Inference Techniques & Applications,
    Vol. 38, No. 9, pp. 1349-1371, 2005.

47. J. Callut and P. Dupont,
    Séparateurs à vaste marge optimisant la fonction F_β,
    Presses Universitaires de Grenoble, pp. 79-91, 2005
    CAp 2005, Conférence d'Apprentissage,
    Nice, France.

48. J. Callut and P. Dupont,
    A Markovian Approach to the Induction of Regular String Distributions,
    Lecture Notes in Artificial Intelligence, No. 3264, Springer, pp. 77--90, 2004
    7th International Colloquium on Grammatical Inference (ICGI),
    Grammatical Inference: Algorithms and Applications.
    Athens, Greece.

49. M. Saerens, F. Fouss, L. Yen and P. Dupont,
    The Principal Components Analysis of a Graph, and its Relationship to Spectral Clustering,
    Lecture Notes in Artificial Intelligence, No. 3201, Springer, pp. 371-383, 2004
    15th European Conference on Machine Learning (ECML),
    Pisa, Italy.

50. C. Kermorvant, C. de la Higuera and P. Dupont,
    Learning typed automata from automatically labeled data,
    Journal Électronique d'Intelligence Artificielle,
    Vol. 6-45, 2004.

51. C. Kermorvant, C. de la Higuera and P. Dupont,
    Improving probabilistic automata learning with Additional Knowledge,
    Lecture Notes in Computer Science, No. 3138, Springer-Verlag, pp. 260-268, 2004
    Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004),
    Lisbon, Portugal.

52. C. Kermorvant, C. de la Higuera and P. Dupont,
    Construction de Modèles de Langages par Inférence d'Automates Typés à partir de Données Etiquetées Automatiquement,
    Presses Universitaires de Grenoble, pp. 77--90, 2003
    Conférence d'Apprentissage,
    Laval, France.

53. P. Dupont, F. Denis and Y. Esposito,
    Links between Probabilistic Automata and Hidden Markov Models: probability distributions, learning models and induction algorithms,
    UCL-INGI, Research Report 2003-02,
    January 2003.

54. C. Kermorvant and P. Dupont,
    Stochastic grammatical inference with multinomial tests,
    Lecture Notes in Artificial Intelligence, No. 2484, Springer, pp. 149--160, 2002
    6th International Colloquium on Grammatical Inference,
    Grammatical Inference: Algorithms and Applications.
    Amsterdam, The Netherlands.

55. Y. Esposito, A. Lemay, F. Denis and P.Dupont,
    Learning Probabilistic Residual Finite Automata,
    Lecture Notes in Artificial Intelligence, No. 2484, Springer, pp. 77--91, 2002
    6th International Colloquium on Grammatical Inference,
    Grammatical Inference: Algorithms and Applications.
    Amsterdam, The Netherlands.

56. C. Kermorvant and P. Dupont,
    Improved smoothing for Probabilistic Suffix Trees seen as variable order Markov chains,
    Lecture Notes in Artificial Intelligence, No. 2430, Springer, pp. 185--194, 2002
    13th European Conference on Machine Learning (ECML),
    Helsinki, Finland.

57. C. Kermorvant and P. Dupont,
    Mélanges de Chaînes de Markov lissées pour la détection de domaines dans les protéines,
    Journées ouvertes biologie informatique mathématique,
    Saint-Malo, France, 2002.

58. C. Kermorvant and P. Dupont,
    Inférence d'automates et correction d'erreurs pour la classification des protéines,
    Conférence d'Apprentissage, pp. 269--278, 2001
    Grenoble, France,

59. F. Thollard, P. Dupont and C. de la Higuera,
    Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality,
    Seventeenth International Conference on Machine Learning (ICML),
    Morgan Kauffman, pp. 975 -- 982, 2000
    Stanford University, USA.

60. P. Dupont and J.-C. Amengual,
    Smoothing probabilistic automata: an error-correcting approach,
    Lecture Notes in Artificial Intelligence, No. 1891, Springer, pp. 51-64, 2000
    5th International Colloquium on Grammatical Inference,
    Grammatical Inference: Algorithms and Applications,
    Lisbon, Portugal.

61. F. Thollard and P. Dupont,
    Inférence Grammaticale Probabiliste utilisant la divergence de Kullback-Leibler et un principe de minimalité,
    Conférence d'Apprentissage, Hermes Science, pp. 259 -- 275, 2000
    Saint-Etienne, France,

62. F. Thollard and P. Dupont,
    Entropie relative et algorithmes d'inférence grammaticale probabiliste,
    Conférence d'Apprentissage, AFIA, pp. 115 -- 121, 1999
    Palaiseau, France.

63. L. Miclet, J. Chodorowski and P. Dupont,
    Apprentissage et évaluation de modèles de langage par des techniques de correction d'erreurs,
    Conférence d'Apprentissage, AFIA, pp. 253 -- 262, 1999
    Palaiseau, France.

64. P. Dupont and L. Chase,
    Using symbol clustering to improve probabilistic automaton inference,
    Lecture Notes in Artificial Intelligence, No. 1433, Springer, pp. 232 -- 243, 1998
    4th International Colloquium on Grammatical Inference,
    Ames, Iowa, USA.

65. P. Dupont and L. Miclet,
    Inférence grammaticale régulière : fondements théoriques et principaux algorithmes,
    Technical Report RR-3449, INRIA,
    1998.

66. P. Dupont,
    Polynomial Regular Inference,
    Technical Report EURISE 9705,
    1997.

67. P. Dupont,
    Incremental Regular Inference,
    Lecture Notes in Artificial Intelligence, No. 1147, Springer, pp. 222 -- 237, 1996
    3rd International Colloquium on Grammatical Inference,
    Grammatical Inference : learning syntax from sentences,
    Montpellier, France.

68. P. Dupont,
    Utilisation et Apprentissage de Modèles de Langage pour la Reconnaissance de la Parole Continue,
    Ecole Nationale Supérieure des Télécommunications, Paris, France
    February 1996, 247 pages.

69. L. Miclet, P. Dupont and S. Vial,
    Inférence Grammaticale Régulière : méthodes semi-itératives et mesure de performance,
    Journées Francophones sur l'Apprentissage, pp. 103--106, 1995
    Grenoble, France.

70. P. Dupont, L. Miclet and E. Vidal,
    What is the search space of the regular inference?,
    Lecture Notes in Artificial Intelligence, No. 862, Springer, pp. 25--37, 1994
    2nd International Colloquium on Grammatical Inference,
    Grammatical Inference and Applications,
    Alicante, Spain.

71. P. Dupont,
    Regular Grammatical Inference from Positive and Negatives Samples by Genetic Search : the GIG method,
    Lecture Notes in Artificial Intelligence, No. 862, Springer, pp. 236--245, 1994
    2nd International Colloquium on Grammatical Inference,
    Grammatical Inference and Applications,
    Alicante, Spain.

72. P. Dupont and F. Pinson,
    Inférence Grammaticale Régulière par Optimisation Génétique à partir d'échantillons positifs et négatifs : la méthode GIG,
    Journées Francophones sur l'Apprentissage, pp. G1--G13, 1994,
    Strasbourg, France.

    Bioinformatics - Biomedical data analysis

    Full papers

73. A. Gerniers, S. Nijssen and P. Dupont,
    scCross: efficient search for rare subpopulations across multiple single-cell samples,
    Bioinformatics, Vol. 40, No.6
    doi:10.1093/bioinformatics/btae371, 2024.

74. A. Gerniers and P. Dupont,
    MicroCellClust 2: a hybrid approach for multivariate rare cell mining in large-scale single-cell data,
    IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Vol. 1, pp. 148-153, 2022,
    Las Vegas, NV, USA.
    [PDF]

75. A. Gerniers, O. Bricard and P. Dupont,
    MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data,
    Bioinformatics,
    doi:10.1093/bioinformatics/btab239, 2021.

76. V. Branders, P. Schaus and P.Dupont,
    Identifying gene-specific subgroups: an alternative to biclustering,
    BMC Bioinformatics,
    doi:10.1186/s12859-019-3289-0, Vol. 20, 625, 2019.

77. S. Branders and P. Dupont,
    A balanced hazard ratio for risk group evaluation from survival data,
    Statistics in Medicine, doi: 10.1002/sim.6505, Vol. 34, Issue 17, pp. 2528-2543, April 20, 2015,
    [Preprint] [Supplementary materials]

78. C. Lombard, F. André, J. Paul, C. Wanty, O. Vosters, P. Bernard, C. Pilette, P. Dupont, E. Sokal, F. Smets,
    Clinical parameters vs cytokine profiles as predictive markers of IgE-mediated allergy in young children,
    PLoS One, Vol. 10, No. 7, pp e0132753, July 27, 2015,
    [PDF]

79. Bernard R. Lauwerys, Daniel Hernandez-Lobato, Pierre Gramme, Julie Ducreux, Adrien Dessy, Isabelle Focant, Jérôme Ambroise, Bertrand Bearzatto, Adrien Nzeusseu Toukap, Benoît Van den Eynde, Dirk Elewaut, Jean-Luc Gala, Patrick Durez, Frédéric A. Houssiau, Thibault Helleputte, Pierre Dupont,
    Heterogeneity of synovial molecular patterns in patients with arthritis,
    PLoS One, Vol. 10, No. 4, pp e0122104, April 30, 2015,
    [PDF]

80. R. Boidot, S. Branders, T. Helleputte, L. Illan Rubio, P. Dupont and O. Feron,
    A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling
    Oncotarget, Vol. 5, No. 16, pp. 6947-6963, July 2014.
    [PDF] [Suppl]

81. M. Grandjean, A Sermeus, S. Branders, F. Defresne, M. Dieu, P. Dupont, M. Raes, M. De Ridder, O. Feron,
    Hypoxia Integration in the Serological Proteome Analysis Unmasks Tumor Antigens and Fosters the Identification of Anti-Phospho-eEF2 Antibodies as Potential Cancer Biomarkers,
    PLOS One Vol.8, No.10, e76508, 2013.

82. E. Seront, S. Rottey, B. Sautois, J. Kerger; L. A. D'Hondt, V. Verschaeve, J-L. Canon, C. Dopchie, J. M. Vandenbulcke, N. Whenham, J. C. Goeminne, M. Clausse, D. Verhoeven, P. Glorieux, S. Branders, P. Dupont, J. Schoonjans, O. Feron, J-P. Machiels,
    Phase II study of Everolimus in patients with locally advanced or metastatic transitional cell carcinoma of the urothelial tract: clinical activity, molecular response and biomarkers,
    Annals of Oncology, Vol. 23, No. 10, pp. 2663-70, 2012.

83. K. Faust, P. Dupont, J. Callut, J. van Helden,
    Pathway discovery in metabolic networks by subgraph extraction,
    Bioinformatics, Vol. 26, No. 9, pp 1211-1218, 2010.

84. T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, Y. Saeys,
    Robust biomarker identification for cancer diagnosis with ensemble feature selection methods,
    Bioinformatics, Vol. 26, No. 3, pp. 392-398, 2010.

    Short papers, communications and patents

85. A. Dessy and P. Dupont,
    Computationally Efficient Test for Gene Set Dysregulation,
    Proceedings of the 8th international workshop on Machine Learning and System Biology (MLSB'14), pp. 31-34,
    Strasbourg, France, 6-7 September 2014.

86. O. Feron, R. Boidot, S. Branders, P. Dupont, T. Helleputte,
    Signature of cycling hypoxia and use thereof for the prognosis of cancer,
    WO/2015/015000, [PDF], Publication Date: 05/02/2015.

87. C. Lombard, F. André, C. Wanty, J. Paul, P. Dupont, E. Sokal, F. Smets,
    Differences in the cytokine profiles of cord blood mononuclear cells from allergic and non-allergic infants (Poster) ,
    European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN'12),
    Stockholm, Sweden, April 27-28, 2012.

88. I. Focant, D. Hernández-Lobato, J. Ducreux, P. Durez, A. Nzeusseu Toukap, D. Elewaut, F. Houssiau, P. Dupont and B. Lauwerys,
    Feasibility of a Molecular Diagnosis of Arthritis Based on the Identification of Specific Transcriptomic Profiles in Knee Synovial Biopsies,
    Arthritis & Rheumatism, Vol 63, No. 10 (Supplement), pp. S751,
    ACR/ARHP Annual Scientific Meeting, Chicago, Illinois, Nov 4-9, 2011.

89. N. Touleimat, D. Hernández-Lobato and P. Dupont,
    Variance estimators for t-Test ranking influence the stability and predictive performance of microarray gene signatures (Poster) ,
    European Conference on Computational Biology (ECCB10),
    Ghent, Belgium, September 26-29, 2010.

90. K. Faust, D. Croes, P. Dupont, J. van Helden,
    Predicting metabolic pathways from bacterial operons and regulons, (Poster)
    European Conference on Computational Biology (ECCB10),
    Ghent, Belgium, September 26-29, 2010.

91. P. Dupont, S. Gaulis and T. Helleputte,
    A Method for Classifying a Cancer Patient as Responder or Non-responder to Immunotherapy (Patent) ,
    WO/2010/029174, March 18, 2010.

92. T. Helleputte and P. Dupont,
    Biomarker Selection by Transfer Learning with Linear Regularized Models, (Poster)
    Third International Workshop on Machine Learning in Systems Biology (MLSB), pp. 159-160
    Ljubljana, Slovenia, September 5-6, 2009.

93. T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys,
    Robust biomarker identification for cancer diagnosis using ensemble feature selection methods,
    Third International Workshop on Machine Learning in Systems Biology (MLSB), pp. 135
    Ljubljana, Slovenia, September 5-6, 2009.

94. K. Faust, J. Callut, P. Dupont and J. van Helden,
    Inference of pathways from metabolic networks by subgraph extraction,
    Second International Workshop on Machine Learning in Systems Biology (MLSB), pp. 27-38
    Brussels, September 13-14, 2008.

95. J. Louahed, S. Gaulis, T. Helleputte, P. Dupont, O. Gruselle, A. Spatz, W. Kruit, B. Dréno, F. Lehmann, V. Brichard,
    Clinical response to the MAGE-A3 immunotherapeutic in metastatic melanoma patients is associated with a specific gene profile present prior to treatment,
    Annals of Oncology, Vol. 19, pp. viii61-viii62, 2008,
    European Society for Medical Oncology (ESMO) Congress, Stockholm, Sweden.

96. T. Helleputte and P. Dupont,
    A comparative study of normalization and feature selection techniques for breast cancer prognosis from gene expressions (Poster) ,
    Benelux Bioinformatics Conference (BBC'07),
    November 12-13, Leuven, Belgium, 2007.

97. K. Faust, P. Dupont, J. Callut and J. Van Helden,
    Inference of pathways from metabolic networks by subgraph extraction (Lecture),
    Benelux Bioinformatics Conference (BBC'07),
    November 12-13, Leuven, Belgium, 2007.

98. Y. Deville, P. Dupont, G. Dooms, J.-N.Monette, P. Schaus, S. Wodak and S. Zampelli,
    BioEdge: a tool box for advanced analyses of biochemical networks (Poster) ,
    Benelux Bioinformatics Conference (BBC'07),
    November 12-13, Leuven, Belgium, 2007.

99. P. Dupont and D. Snyers,
    Efficient Dynamic Expansion of Context-Free Grammar in Speech Recognition,
    in Overview of Research in Speech Recognition at PRLB in 1988,
    Philips Research Laboratory, Brussels, Belgium, pp. 32--68, 1989.

    Constraint programming

100. P. Schaus, Y. Deville and P. Dupont,
     Bound-Consistent Deviation Constraint,
     Lecture Notes in Computer Science, No. 4741, Springer, pp. 620-634, 2007
     13th International Conference on Principles and Practice of Constraint Programming,
     Providence, RI, USA, September 23-27.

101. S. Zampelli, Y. Deville, C. Solnon, S. Sorlin and P. Dupont,
     Filtering for Subgraph Isomorphism,
     Lecture Notes in Computer Science, No. 4741, Springer, pp. 728-742, 2007
     13th International Conference on Principles and Practice of Constraint Programming,
     Providence, RI, USA, September 23-27.

102. J.-N. Monette, P. Schaus, S. Zampelli, Y. Deville and P. Dupont,
     A CP Approach to the Balanced Academic Curriculum Problem,
     7th International Workshop on Symmetry and Constraint Satisfaction Problems, pp. 56-63, 2007
     Providence, RI, USA, September 23.

103. S. Zampelli, Y. Deville, C. Solnon, S. Sorlin and P. Dupont,
     Filtrage pour l'isomorphisme de sous-graphe,
     Journées Francophones de Programmation par Contraintes, pp. 285-296, 2007
     Rocquencourt, France, June 4-6.

104. P. Schaus, Y. Deville and P. Dupont,
     La Contrainte Déviation,
     Journées Francophones de Programmation par Contraintes, pp. 173-182, 2007
     Rocquencourt, France, June 4-6.

105. J.-N. Monette, Y. Deville, and P. Dupont,
     Un propagateur basé sur les positions pour le problème d'Open-Shop,
     Journées Francophones de Programmation par Contraintes, , pp. 255-264, 2007
     Rocquencourt, France, June 4-6.

106. P. Schaus, Y. Deville, P. Dupont and J.-C. Régin,
     The Deviation Constraint,
     Lecture Notes in Computer Science, No. 4510, Springer, pp. 260-274, 2007
     4th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems,
     Brussels, Belgium, May 23-26.

107. J.-N. Monette, Y. Deville, and P. Dupont,
     A Position-Based Propagator for the Open-Shop Problem,
     Lecture Notes in Computer Science, No. 4510, Springer, pp. 186-199, 2007
     4th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems,
     Brussels, Belgium, May 23-26.

108. P. Schaus, Y. Deville, P. Dupont and J.-C. Régin,
     Simplification and extension of the SPREAD Constraint,
     Trends in Constraint Programming, pp. 95-99, ISTE Hermes, 2007
     F. Benhamou, N. Jussien and B. O'Sullivan (Eds).

109. S. Zampelli, Y. Deville and P. Dupont,
     Symmetry Breaking in Subgraph Pattern Matching,
     Trends in Constraint Programming, Chapter 10, pp. 203-218, ISTE Hermes, 2007
     F. Benhamou, N. Jussien and B. O'Sullivan (Eds).

110. P. Schaus, Y. Deville, P. Dupont and J.-C. Régin,
     Simplification and extension of the SPREAD Constraint,
     Third International Workshop on Constraint Propagation And Implementation, pp. 77-91, 2006
     Nantes, France, September 25.

111. S. Zampelli, Y. Deville and P. Dupont,
     Symmetry Breaking in Subgraph Pattern Matching,
     Sixth International Workshop on Symmetry in Constraint Satisfaction Problems, pp. 31-38, 2006.
     Nantes, France, September 25.

112. S. Zampelli, Y. Deville and P. Dupont,
     Elimination des symétries pour l'appariement de graphes,
     Deuxièmes Journées Francophones de Programmation par Contraintes, pp. 357-367, 2006
     Nîmes, France, June 7-9.

113. Y. Deville, G. Dooms, S. Zampelli and P. Dupont,
     CP(Graph+Map) for Approximate Graph Matching,
     1st International Workshop on Constraint Programming Beyond Finite Integer Domains, pp. 31-47, 2005
     Sitges, Barcelona, Spain, October 1.

114. G. Dooms, Y. Deville and P. Dupont,
     Constrained metabolic network analysis: discovering pathways using CP(Graph),
     Workshop on Constraint Based Methods for Bioinformatics, pp. 29-35, 2005
     Sitges, Barcelona, Spain, October 5.

115. S. Zampelli, Y. Deville and P. Dupont,
     Declarative Approximate Graph Matching Using a Constraint Approach,
     Second International Workshop on Constraint Propagation and Implementation, Vol. 1, pp. 109-124, 2005
     Sitges, Barcelona, Spain, October 1.

116. S. Zampelli, Y. Deville and P. Dupont,
     Approximate Constrained Subgraph Matching,
     Lecture Notes in Computer Science, No. 3709, Springer,
     11th International Conference on Principles and Practice of Constraint Programming, pp. 832-836, 2005
     Sitges, Barcelona, Spain.

117. G. Dooms, Y. Deville and P. Dupont,
     CP(Graph): Introducing a Graph Computation Domain in Constraint Programming,
     Lecture Notes in Computer Science, No. 3709, Springer,
     11th International Conference on Principles and Practice of Constraint Programming, pp. 211-225, 2005
     Sitges, Barcelona, Spain.

118. G. Dooms, Y. Deville and P. Dupont,
     A Mozart implementation of CP(BioNet),
     Lecture Notes in Artificial Intelligence, No. 3389, Springer, pp. 237-250, 2005
     2nd International Mozart/Oz Conference,
     Charleroi, Belgium.

119. G. Dooms, Y. Deville and P. Dupont,
     Recherche de chemins contraints dans les réseaux biochimiques,
     Treizièmes Journées Francophones de Programmation en Logique et de Programmation par Contraintes, pp. 109-128, 2004
     Angers, France, June.

120. G. Dooms, Y. Deville and P. Dupont,
     Constrained Path Finding in Biochemical Networks: a Constraint Programming Approach,
     5th Open Days in Biology, Computer Science and Mathematics, JO-40, 2004
     Montreal, Canada.

121. S. Zampelli, Y. Deville and P. Dupont,
     Finding Patterns in Biochemical Networks,
     5th Open Days in Biology, Computer Science and Mathematics, JO-85, 2004
     Montreal, Canada.

     Pedagogy

122. B. Raucent and C. Vander Borght (editors),
     Être enseignant. Magister ? Metteur en scène ?
     De Boeck, 2006.

123. E. Bourgeois, P. Dupont, M. Frenay, B. Galand, A. Laloux, E. Milgrom, B. Raucent, C. Trullemans, C. Vander Borght, V. Wertz, P. Wouters,
     L'approche par Problèmes et par Projets dans l'Enseignement Supérieur. Impact, Enjeux et Défis,
     Presses Universitaires de Louvain, 2005, Benoît Galand et Mariane Frenay (editors.)

     Speech and language processing

124. M.-C. de Marneffe, C. Archambeau, P. Dupont and M. Verleysen,
     Local Vector-based Models for Sense Discrimination,
     Sixth International Workshop on Computational Semantics, pp. 163-174, 2005
     Tilburg, The Netherlands, January 12-14.

125. M.-C. de Marneffe and P. Dupont,
     Comparative study of statistical word sense discrimination,
     7th International Conference on Statistical Analysis of Textual Data, pp. 270--281, 2004
     Louvain-La-Neuve, Belgium.

126. J. González, A. Juan, P. Dupont, E. Vidal and F. Casacuberta,
     A Bernoulli mixture model for word categorisation,
     Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Vol. 1, pp. 165-170, 2001
     Benicássim, Spain.

127. L.J. Boë, F. Bimbot, J.-F. Bonastre and P. Dupont,
     Des évaluations des systèmes de vérification du locuteur à la mise en cause des expertises vocales en identification juridique,
     Langues, Vol.2, No.4, pp 270--288, 1999.

128. P. Dupont and R. Rosenfeld,
     Lattice based language models,
     Technical Report, Carnegie Mellon University, CMU-CS-97-173,
     1997.

129. P. Dupont,
     Interpolated Word and Class Bigram Models for Spanish Conversational Speech Recognition,
     IEEE workshop on Automatic Speech Recognition, pp. 121--122, 1995
     Snowbird, Utah, USA.

130. D. Sadek, P. Bretier, V. Cadoret, A. Cozannet, P. Dupont, A. Ferrieux and F. Panaget,
     A Cooperative Spoken Dialogue System Based on a Rational Agent Model: A First Implementation on the AGS Application,
     ESCA Tutorial and Research Workshop on Spoken Dialogue Systems, , 1995
     Visgo, Denmark.

131. P. Dupont,
     Dynamic Use of Syntactical Knowledge in Continuous Speech Recognition,
     European Conference on Speech Communication and Technology, pp. 1959--1962, 1993
     Berlin, Germany.

132. P. Dupont,
     Efficient Integration of Context-Free Based Language Models in Continuous Speech Recognition,
     New Advances and Trends in Speech Recognition and Coding, NATO ASI, pp. 179--182, 1993
     Granada, Spain.

133. P. Dupont and Y. Kamp,
     Guiding Speech Recognition by a Language Model,
     A Logic Based Approach to Artificial Intelligence,
     chap. 1, pp. 1--48, Chichester, Wiley, 1991
     From Natural Language Processing to Logic for Expert Systems,
     A. Thayse (editor).

134. P. Dupont and A.Thayse,
     Montague's Semantics and Boolean Semantics for Natural Language Representation,
     Philips Journal of Research,
     Vol.45, No.1, pp. 41--66, 1990.

135. P. Dupont and Y. Kamp,
     Reconnaissance de la parole pilotée par un modèle linguistique,
     Approche logique de l'intelligence artificielle,
     Vol. 3, chap. 1, pp. 1--61. -- Paris, Bordas, Dunod Informatique, 1990
     Du traitement de la langue à la logique des systèmes experts,
     A. Thayse (éditeur).

Pierre Dupont