Publications

(Don't hesitate to follow the links to the pdf files in our University repository. Downloads of unlocked files are fully anonymous and, for the locked files, I only need to perform one mouse click to send you a copy. If you have any problem with the system, feel free to contact me directly to obtain a copy of a paper.)

2010


Louppe, G., & Geurts, P. (2010, December 11). A zealous parallel gradient descent algorithm. Paper presented at NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, Whistler, Canada.
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010). Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE, 5(9), 12776.
Liao, Y., Geurts, P., & Leduc, G. (2010). Network Distance Prediction Based on Decentralized Matrix Factorization. Lecture Notes in Computer Science, 6091, 15-26.
Marée, R., Denis, P., Wehenkel, L., & Geurts, P. (2010). Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies. ACM Proceedings MIR 2010.
El Khayat, I., Geurts, P., & Leduc, G. (2010). Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wireless Networks, 16(2), 273-290.
De Lobel, L., Geurts, P., Baele, G., Castro-Giner, F., Kogevinas, M., & Van Steen, K. (2010). A screening methodology based on Random Forests to improve the detection of gene-gene interactions. European Journal of Human Genetics.

2009


Cornélusse, B., Geurts, P., & Wehenkel, L. (2009, December 12). Tree based ensemble models regularization by convex optimization. Paper presented at NIPS-09 workshop on Optimization for Machine Learning, Whistler, Canada.
Geurts, P., Irrthum, A., & Wehenkel, L. (2009). Supervised learning with decision tree-based methods in computational and systems biology. Molecular Biosystems, 5(12), 1593-1605.
Liao, Y., Kaafar, M. A., Gueye, B., Cantin, F., Geurts, P., & Leduc, G. (2009). Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning. Lecture Notes in Computer Science, 5550, 352-363.
Dumont, M., Marée, R., Wehenkel, L., & Geurts, P. (2009). Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. Proc. International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 196-203).
Marée, R., Geurts, P., & Wehenkel, L. (2009). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. IPSJ Transactions on Computer Vision and Applications, 1.
De Seny, D., Ribbens, C., Cobraiville, G., Meuwis, M.-A., Marée, R., Geurts, P., Wehenkel, L., Louis, E., Merville, M.-P., Fillet, M., & Malaise, M. (2009). Protéomique par SELDI-TOF-MS des maladies inflammatoires articulaires: identification des protéines S100 comme protéines d'intérêt. Revue Médicale de Liège, 64(Spec No), 29-35.
Marée, R., Stevens, B., Geurts, P., Guern, Y., & Mack, P. (2009). A Machine Learning Approach for Material Detection in Hyperspectral Images. Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09). IEEE.

2008


Botta, V., Hansoul, S., Geurts, P., & Wehenkel, L. (2008). Raw genotypes vs haplotype blocks for genome wide association studies by random forests. Proc. of MLSB 2008, second workshop on Machine Learning in Systems Biology.
Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. Proc. of FSDM08, ECML/PKDD Workshop on New challenges for feature selection in data mining and knowledge discovery.
Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. JMLR: Workshop and Conference Proceedings, 4, 60-73.
Levels, J. H., Marée, R., Geurts, P., Kuivenhoven, J. A., Wehenkel, L., Kastelein, J. J., & Meijers, J. C. M. (2008). Compositional protein analysis of HDL by SELDI-TOF MS during experimental endotoxemia. Paper presented at 77th European Atherosclerosis Society Congress, Istanbul, Turkey.
Meuwis, M.-A., Fillet, M., Lutteri, L., Marée, R., Geurts, P., De Seny, D., Malaise, M., Chapelle, J.-P., Wehenkel, L., Belaiche, J., Merville, M.-P., & Louis, E. (2008). Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clinical Biochemistry, 41, 960-967.
Meuwis, M.-A., Fillet, M., Lutteri, L., Marée, R., Geurts, P., De Seny, D., Malaise, M., Chapelle, J.-P., Wehenkel, L., Belaiche, J., Merville, M.-P., & Louis, E. (2008). Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clinical Biochemistry, 41(12), 960-7.

2007


Del Angel, A., Geurts, P., Ernst, D., Glavic, M., & Wehenkel, L. (2007). Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing, 70(16-18), 2668-2678.
Marée, R., Dumont, M., Geurts, P., & Wehenkel, L. (2007, July 22). Random Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation. Paper presented at 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 6th European Conference on Computational Biology (ECCB), Vienna, Austria.
El Khayat, I., Geurts, P., & Leduc, G. (2007). Machine-learnt versus analytical models of TCP throughput. Computer Networks, 51(10), 2631-2644.
Geurts, P., Touleimat, N., Dutreix, M., & d'Alche-Buc, F. (2007). Inferring biological networks with output kernel trees. BMC Bioinformatics, 8(Suppl. 2), 4.
Meuwis, M.-A., Fillet, M., Geurts, P., De Seny, D., Lutteri, L., Chapelle, J.-P., Bours, V., Wehenkel, L., Belaiche, J., Malaise, M., Louis, E., & Merville, M.-P. (2007). Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling. Biochemical Pharmacology, 73(9), 1422-1433.
Dumont, M., Marée, R., Geurts, P., & Wehenkel, L. (2007). Random Subwindows and Multiple Output Decision Trees for Generic Image Annotation. Paper presented at 16th Annual Machine Learning Conference of Belgium and The Netherlands, Amsterdam, NL.
Geurts, P., Wehenkel, L., & d'Alché-Buc, F. (2007). Gradient boosting for kernelized output spaces. Proceedings of the 24th International Conference on Machine Learning (pp. 289-296).
Marée, R., Geurts, P., & Wehenkel, L. (2007). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. Proc. 8th Asian Conference on Computer Vision (ACCV), LNCS (pp. 611-620). Springer-Verlag.
Marée, R., Geurts, P., & Wehenkel, L. (2007). Random subwindows and extremely randomized trees for image classification in cell biology. BMC Cell Biology, 8(Suppl. 1).
Meuwis, M.-A., Fillet, M., Geurts, P., De Seny, D., Lutteri, L., Chapelle, J.-P., Bours, V., Wehenkel, L., Belaiche, J., Malaise, M., Louis, E., & Merville, M.-P. (2007). Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling. Biochemical Pharmacology, 73(9), 1422-33.

2006


El Khayat, I., Geurts, P., & Leduc, G. (2006). On the accuracy of analytical models of TCP throughput. Lecture Notes in Computer Science, 3976, 488-500.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3-42.
Auvray, V., Geurts, P., & Wehenkel, L. (2006). A Semi-Algebraic Description of Discrete Naive Bayes Models with Two Hidden Classes. Proc. Ninth International Symposium on Artificial Intelligence and Mathematics.
Geurts, P., Marée, R., & Wehenkel, L. (2006). Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data. Proceedings of the Machine Learning Conference of Belgium and The Netherlands (Benelearn) (pp. 15-23).
Geurts, P., Touleimat, N., Dutreix, M., & d Alché-Buc, F. (2006). Completion of biological networks: the output kernel trees approach. Proceedings of the the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology. Helsiinki, Finland.
Geurts, P., Wehenkel, L., & d Alché-Buc, F. (2006). Kernelizing the output of tree-based methods. Proceedings of the 23rd International Conference on Machine Learning (pp. 345--352). Acm.
Geurts, P., Wehenkel, L., & d'Alché-Buc, F. (2006). OK3: Méthode d’arbres à sortie noyau pour la prédiction de sorties structurées et l’apprentissage de noyau. Proc. of CAP (Conférence francophone d'apprentissage) (pp. 16).
Marée, R., Geurts, P., & Wehenkel, L. (2006). Biological Image Classification with Random Subwindows and Extra-Trees. Paper presented at Bio-Image Informatics (Workshop on Multiscale Biological Imaging, Data Mining & Informatics), Santa Barbara, USA.
Quach, M., Geurts, P., & d Alché-Buc, F. (2006). Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data. Proc. of the 14th European Symposium on Artificial Neural Networks.
Wehenkel, L., Ernst, D., & Geurts, P. (2006). Ensembles of extremely randomized trees and some generic applications. Proceedings of Robust Methods for Power System State Estimation and Load Forecasting.
Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). About automatic learning for advanced sensing, monitoring and control of electric power systems. Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry.
Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). Automatic learning of sequential decision strategies for dynamic security assessment and control. Proceedings of the IEEE Power Engineering Society General Meeting 2006.

2005


El Khayat, I., Geurts, P., & Leduc, G. (2005). Improving TCP in wireless networks with an adaptive machine-learnt classifier of packet loss causes. Lecture Notes in Computer Science, 3462, 549-560.
Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6, 503-556.
De Seny, D., Fillet, M., Meuwis, M.-A., Geurts, P., Lutteri, L., Ribbens, C., Bours, V., Wehenkel, L., Piette, J., Malaise, M., & Merville, M.-P. (2005). Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach. Arthritis and Rheumatism, 52(12), 3801-12.
Ernst, D., Glavic, M., Geurts, P., & Wehenkel, L. (2005). Approximate value iteration in the reinforcement learning context. Application to electrical power system control. International Journal of Emerging Electrical Power Systems, 3(1).
Geurts, P. (2005). Bias vs. variance decomposition for regression and classification. In O., Maimon & L., Rokach (Eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Academic Publishers.
Geurts, P., Blanco Cuesta, A., & Wehenkel, L. (2005). Segment and combine approach for Biological Sequence Classification. Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005) (pp. 194--201).
Geurts, P., Fillet, M., De Seny, D., Meuwis, M.-A., Malaise, M., Merville, M.-P., & Wehenkel, L. (2005). Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics, 21(14), 3138-45.
Geurts, P., & Wehenkel, L. (2005). Closed-form dual perturb and combine for tree-based models. Proceedings of the International Conference on Machine Learning (ICML 2005).
Geurts, P., & Wehenkel, L. (2005). Segment and combine approach for non-parametric time-series classification. Lecture Notes in Computer Science, 3721, 478-485.
Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Biomedical image classification with random subwindows and decision trees. Computer Vision for Biomedical Image Applications (pp. 220-229). Berlin: Springer-Verlag Berlin.
Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Decision Trees and Random Subwindows for Object Recognition. ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005).
Marée, R., Geurts, P., Piater, J., Wehenkel, L., Schmid, C. (Ed.), Soatto, S. (Ed.), & Tomasi, C. (Ed.). (2005). Random Subwindows for Robust Image Classification. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (pp. 34--40).

2004


Geurts, P., El Khayat, I., & Leduc, G. (2004, November). A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks. USA: IEEE.
de Seny, D., Fillet, M., Meuwis, M.-A., Lutteri, L., Geurts, P., Wehenkel, L., Bours, V., Piette, J., Malaise, M., & Merville, M.-P. (2004). Discovery of new rheumatoid arthritis biomarkers using SELDI-TOF-MS ProteinChip approach. Arthritis and Rheumatism, 50(9, Suppl. S), 124.
Marée, R., Geurts, P., Piater, J., Wehenkel, L., Hong, K.-S. (Ed.), & Zhang, Z. (Ed.). (2004). A generic approach for image classification based on decision tree ensembles and local sub-windows. Proceedings of the 6th Asian Conference on Computer Vision (pp. 860-865). Asian Federation of Computer Vision Societies (AFCV).

2003


Ernst, D., Geurts, P., & Wehenkel, L. (2003). Iteratively extending time horizon reinforcement learning. Machine Learning: ECML 2003, 14th European Conference on Machine Learning (pp. 96-107). Berlin: Springer-Verlag Berlin.
Geurts, P. (2003). Traitement de données volumineuses par ensemble d'arbres aléatoires. Revue des nouvelles technologies de l'information, Numéro spécial entreposage et fouille de données, 1, 111-122.
Marée, R., Geurts, P., Visimberga, G., Piater, J., Wehenkel, L., Coenen, F. (Ed.), Preece, A. (Ed.), & Macintosh, A. (Ed.). (2003). An empirical comparison of machine learning algorithms for generic image classification. Proceedings of the 23rd SGAI international conference on innovative techniques and applications of artificial intelligence, Research and development in intelligent systems XX (pp. 169-182). Springer.
Marée, R., Geurts, P., & Wehenkel, L. (2003). Une méthode générique pour la classification automatique d'images à partir des pixels. Revue des Nouvelles Technologies de l'Information, 1, 227-238.

2002


Geurts, P. (2002). Contributions to decision tree induction: bias/variance tradeoff and time series classification. Unpublished doctoral thesis, University of Liège, Belgium.

2001


Geurts, P. (2001). Dual Perturb and Combine Algorithm. Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics (pp. 196-201). Key-West, Florida.
Geurts, P. (2001). Pattern extraction for time-series classification. Proceedings of PKDD 2001, 5th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 115-127). Freiburg: Springer-Verlag.
Geurts, P., Olaru, C., & Wehenkel, L. (2001). Improving the bias/variance tradeoff of decision trees - towards soft tree induction. Engineering intelligent systems, 9, 195-204.

2000


Geurts, P. (2000). Some enhancements of decision tree bagging. Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 136-147). Lyon, France: Springer-Verlag.
Geurts, P., & Wehenkel, L. (2000). Investigation and reduction of discretization Variance in decision tree induction. Proceedings of ECML 2000, European Conference on Machine Learning (pp. 162-170). Springer-Verlag.
Geurts, P., & Wehenkel, L. (2000). Temporal machine learning for switching control. Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 401-408). Lyon, France: Springer-Verlag.

1999


Olaru, C., Geurts, P., & Wehenkel, L. (1999). Data mining tools and application in power system engineering. Proceedings of the 13th Power System Computation Conference, PSCC99 (pp. 324-330). Trondheim, Norway.

1998


Geurts, P., & Wehenkel, L. (1998). Early prediction of electric power system blackouts by temporal machine learning. Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis" (pp. 21-27). Madison (Wisconsin).
Geurts, P., & Wehenkel, L. (1998). Visualizing dynamic power system scenarios for data mining. Proceedings of LESCOPE 98, Large Engineering Syst. Conf. on Power Engineering (pp. 217-224). Halifax.
Years: