Publications

See here for a more complete list (that includes abstracts, posters, etc.) and here for my google scholar page.

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.

2020


Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2020, July). The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. International Joint Conference on Neural Networks (IJCNN 2020).
Mormont, R., Geurts, P., & Marée, R. (2020). Multi-task pre-training of deep neural networks for digital pathology. IEEE Journal of Biomedical and Health Informatics.

2019


Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2019, December). Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. Advances in Neural Information Processing Systems.
Sabatelli, M., Kestemont, M., & Geurts, P. (2019, October). Improving the Training of Deep Convolutional Neural Networks for Art Classification: from Transfer Learning to Multi-Task Learning. Paper presented at The 6th Digital Humanities (DH) Benelux Conference.
Faux, P., Geurts, P., & Druet, T. (2019). A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes. Frontiers in Genetics, 10, 562.
Huynh-Thu, V. A., & Geurts, P. (2019). Unsupervised gene network inference with decision trees and Random forests. In G., Sanguinetti & V. A., Huynh-Thu (Eds.), Gene Regulatory Networks. New York, NY: Humana Press.
Meyer, F., Wehenkel, M., Phillips, C., Geurts, P., HUSTINX, R., Bernard, C., Bastin, C., Salmon, E., & Alzheimer's Disease NeuroImaging Initiative. (2019). Characterization of a temporoparietal junction subtype of Alzheimer’s disease. Human Brain Mapping, 40, 4279-4286.

2018


Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2018, December 07). Deep Quality Value (DQV) Learning. Advances in Neural Information Processing Systems, Deep Reinforcement Learning Workshop.
Sabatelli, M., Kestemont, M., Daelemans, W., & Geurts, P. (2018, September). Deep Transfer Learning for Art Classification Problems. European Conference on Computer Vision (ECCV), 4th Workshop on Computer VISion for ART Analysis (VISART IV).
Huynh-Thu, V. A., & Geurts, P. (2018). dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific Reports, 8, 3384.
Vandaele, R., Aceto, J., Muller, M., Peronnet, F., Debat, V., Wang, C.-W., Huang, C.-T., Jodogne, S., Martinive, P., Geurts, P., & Marée, R. (2018). Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. Scientific Reports, 8(1), 538.
Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (2018). Random Subspace with Trees for Feature Selection Under Memory Constraints. In A., Storkey & F., Perez-Cruz (Eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (pp. 929--937). Playa Blanca, Espagne: PMLR.
Wehenkel, M., Sutera, A., Bastin, C., Geurts, P.* , & Phillips, C.* . (2018). Random Forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Frontiers in Neuroscience, 12, 411.
* These authors have contributed equally to this work.
Pliakos, K., Geurts, P., & Vens, C. (2018). Global multi-output decision trees for interaction prediction. Machine Learning, 107, 1257-1281.
Wehenkel, M., Bastin, C., Geurts, P., & Phillips, C. (2018). Computer Aided Diagnosis System Based on Random Forests for the Prognosis of Alzheimer’s Disease. 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science. Frontiers Media S.A.
Mormont, R., Geurts, P., & Marée, R. (2018). Comparison of deep transfer learning strategies for digital pathology. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE.
Olivier, F., Sutera, A., Geurts, P., Fonteneau, R., & Ernst, D. (2018). Phase Identification of Smart Meters by Clustering Voltage Measurements. Proceedings of the XX Power Systems Computation Conference (PSCC 2018).

2017


Vecoven, N., Begon, J.-M., Huynh-Thu, V. A., & Geurts, P. (2017). Nets versus trees for feature ranking and gene network inference. Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/231719.
Begon, J.-M., Joly, A., & Geurts, P. (2017). Globally Induced Forest: A Prepruning Compression Scheme. JMLR: Workshop and Conference Proceedings (ICML 2017), 70, 420-428.
Wehenkel, M., Bastin, C., Phillips, C.* , & Geurts, P.* . (2017). Tree Ensemble Methods and Parcelling to Identify Brain Areas Related to Alzheimer’s Disease. 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), proceedings. IEEE.
Vandaele, R., Lallemand, F., Martinive, P., Gulyban, A., Jodogne, S., COUCKE, P., Geurts, P., & Marée, R. (2017). Automated multimodal volume registration based on supervised 3D anatomical landmark detection. International Conference on Computer Vision Theory and Applications (VISAPP).
Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Ernst, D., & Geurts, P. (2017). Simple connectome inference from partial correlation statistics in calcium imaging. In J., Soriano, D., Battaglia, I., Guyon, V., Lemaire, J., Orlandi, & B., Ray (Eds.), Neural Connectomics Challenge (pp. 23-36). Springer.
Azrour, S., Pierard, S., Geurts, P., & Van Droogenbroeck, M. (2017). A two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically. Advanced Concepts for Intelligent Vision Systems.
Layios, N., Delierneux, C., Hego, A., Huart, J., Gosset, C., Lecut, C., Maes, N., Geurts, P., Joly, A., Lancellotti, P., Albert, A., Damas, P., Gothot, A., & Oury, C. (2017). Sepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study. Intensive Care Medicine Experimental, 5(1), 32.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Atak, Z. K., Wouters, J., & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14, 1083–1086.

2016


Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2016). Context-dependent feature analysis with random forests. Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016).
Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016, January 10). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7.
Marée, R., Geurts, P., & Wehenkel, L. (2016). Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study. Pattern Recognition Letters.
Geurts, P., & Wehenkel, L. (2016). Comments on: A random forest guided tour. TEST, 25(2), 247-253.
Begon, J.-M., Joly, A., & Geurts, P. (2016, September 12). Joint learning and pruning of decision forests. Paper presented at The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgique.
Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (2016, September 12). Random subspace with trees for feature selection under memory constraints. Paper presented at The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgique.

2015


Schrynemackers, M., Wehenkel, L., Madan Babu, M., & Geurts, P. (2015). Classifying pairs with trees for supervised biological network inference Molecular BioSystems, 8(11), 2116-2125. (Eprint/Working paper available at http://arxiv.org/abs/1404.6074)
Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2015). Simple connectome inference from partial correlation statistics in calcium imaging. JMLR: Workshop and Conference Proceedings 46:23-35.
Freres, P.* , Wenric, S.* , Boukerroucha, M., Fasquelle, C., Thiry, J., Bovy, N., Struman, I., Geurts, P., Collignon, J., Schroeder, H., Kridelka, F., Lifrange, E., Jossa, V., Bours, V., Josse, C., & Jerusalem, G. (2015, December 29). Circulating microRNA-based screening tool for breast cancer. Oncotarget.
Du, W., Liao, Y., Tao, N., Geurts, P., Fu, X., & Leduc, G. (2015). Rating Network Paths for Locality-Aware Overlay Construction and Routing. IEEE/ACM Transactions on Networking, 23(5), 1661-1673.
Liegeois, R., Ziegler, E., Bahri, M. A., Phillips, C., Geurts, P., Gomez, F., Yeo, T., Vanhaudenhuyse, A., Soddu, A., Laureys, S., & Sepulchre, R. (2015, July). Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints. Brain Structure and Function.
Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Noël, A., Foidart, J.-M., & Maquoi, E. (2015). A Membrane-Type-1 Matrix Metalloproteinase (MT1-MMP) - Discoidin Domain Receptor 1 Axis Regulates Collagen-Induced Apoptosis in Breast Cancer Cells. PloS one, 10(3), 0116006.
Ching Wei, W., Cheng-Ta, H., Meng-Che, H., Chu-Hsing, L., Vandaele, R., Sheng-Wei, C., Wei-Cheng, L., Marée, R., Guoyan, Z., Ghassan, H., Vrtovec, T., Jodogne, S., Geurts, P., Chengwen, C., Hengameh, M., & Bulat, I. (2015). Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Transactions on Medical Imaging.
Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (2015). Phenotype Classification of Zebrafish Embryos by Supervised Learning. PLoS ONE, 10(1), 0116989, 1-20.

2014


Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014, April 02). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE.
Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. Machine Learning and Knowledge Discovery in Databases.
Azrour, S., Pierard, S., Geurts, P., & Van Droogenbroeck, M. (2014). Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 649-654).
Ruyssinck, J., Huynh-Thu, V. A., Geurts, P., Dhaene, T., Demeester, P., & Saeys, Y. (2014, March 25). NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms. PLoS ONE.
Josse, C., Bouznad, N., Geurts, P., Irrthum, A., Huynh-Thu, V. A., Servais, L., Hego, A., Delvenne, P., Bours, V., & Oury, C. (2014). Identification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis. American Journal of Physiology - Gastrointestinal and Liver Physiology, 306, 229-43.
Marchand, G., Huynh-Thu, V. A., Kane, N. C., Arribat, S., Vares, D., Rengel, D., Balzergue, S., Rieseberg, L. H., Vincourt, P., Geurts, P., Vignes, M., & Langlade, N. B. (2014). Bridging physiological and evolutionary time-scales in a gene regulatory network. The New phytologist.
Schepers, K., Mouchet, F., Dirix, V., De Schutter, I., Kersten, J., Verscheure, V., Geurts, P., Mahavir, S., Van Vooren, J.-P., & Mascart, F. (2013). Long-incubation time-interferon-gamma release assays in response to PPD-, ESAT-6- and/or CFP-10 for the diagnosis of Mycobacterium tuberculosis infection in children. Clinical and Vaccine Immunology, 21(2), 111-118.
Potier, D., Davie, K., Hulselmans, G., Naval Sanchez, M., Haagen, L., Huynh-Thu, V. A., Koldere, D., Celik, A., Geurts, P., Christiaens, V., & Aerts, S. (2014). Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference. Cell Reports, 9(6), 2290-2303.

2013


Huynh-Thu, V. A, Wehenkel, L, & Geurts, P. (in press). Gene regulatory network inference from systems genetics data using tree-based methods. Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/156498.
Louppe, G, Wehenkel, L, Sutera, A, & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. Advances in Neural Information Processing Systems 26.
Schrynemackers, M, Kuffner, R, & Geurts, P. (2013). On protocols and measures for the validation of supervised methods for the inference of biological networks. Frontiers in Genetics, 4(262).
Marée, R, Wehenkel, L, & Geurts, P. (2013). Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval. In A., Criminisi & J., Shotton (Eds.), Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition (pp. 125-142). Springer.
Liao, Y, Du, W, Geurts, P, & Leduc, G. (2013). DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. IEEE/ACM Transactions on Networking.
Mikut, R, Dickmeis, T, Driever, W, Geurts, P, Hamprecht, F. A, Kausler, B. X, Ledesma-Carbayo, M. J, Maree, R, Mikula, K, Pantazis, P, Ronneberger, O, Santos, A, Stotzka, R, Strahle, U, & Peyrieras, N. (2013). Automated Processing of Zebrafish Imaging Data: A Survey. Zebrafish, 10(3), 401-421.

2012


Huynh-Thu, V. A, Saeys, Y, Wehenkel, L, & Geurts, P. (2012). Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics, 28(13), 1766-1774.
Louppe, G, & Geurts, P. (2012). Ensembles on Random Patches. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer-Verlag.
Marbach, D, Costello, J. C, Küffner, R, Vega, N. M, Prill, R. J, Camacho, D. M, Allison, K. R, Aderhold, A, Bonneau, R, Chen, Y, Cordero, F, Crane, M, Dondelinger, F, Drton, M, Esposito, R, Foygel, R, de la Fuente, A, Gertheiss, J, Geurts, P, Greenfield, A, Grzegorczyk, M, Haury, A.-C, Holmes, B, Hothorn, T, Husmeier, D, Huynh-Thu, V. A, Irrthum, A, Karlebach, G, Lèbre, S, De Leo, V, Madar, A, Mani, S, Mordelet, F, Ostrer, H, Ouyang, Z, Pandya, R, Petri, T, Pinna, A, Poultney, C. S, Rezny, S, Ruskin, H. J, Saeys, Y, Shamir, R, Sîrbu, A, Song, M, Soranzo, N, Statnikov, A, Vega, N, Vera-Licona, P, Vert, J.-P, Visconti, A, Wang, H, Wehenkel, L, Windhager, L, Zhang, Y, Zimmer, R, Kellis, M, Collins, J. J, & Stolovitzky, G. (2012). Wisdom of crowds for robust gene network inference. Nature Methods, 9, 796-804.
Joly, A, Schnitzler, F, Geurts, P, & Wehenkel, L. (2012). L1-based compression of random forest models. 20th European Symposium on Artificial Neural Networks.
Maes, F, Geurts, P, & Wehenkel, L. (2012, September). Embedding Monte Carlo search of features in tree-based ensemble methods. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, United Kingdom.
Hiard, S, Geurts, P, & Wehenkel, L. (2012). Comparator selection for RPC with many labels. ECAI 2012 : 20th European Conference on Artificial Intelligence : 27-31 August 2012, Montpellier, France (pp. 408-413). Amsterdam, Netherlands: IOS Press.
Schnitzler, F, Ammar, S, Leray, P, Geurts, P, & Wehenkel, L. (2012). Approximation efficace de mélanges bootstrap d’arbres de Markov pour l’estimation de densité. In L., Bougrain (Ed.), Actes de la 14e Conférence Francophone sur l'Apprentissage Automatique (CAp 2012) (pp. 207-222).

2011


Liao, Y., Du, W., Geurts, P., & Leduc, G. (2011). Decentralized Prediction of End-to-End Network Performance Classes. Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT). ACM.
Schnitzler, F., ammar, S., leray, P., Geurts, P., & Wehenkel, L. (2011). Efficiently approximating Markov tree bagging for high-dimensional density estimation. In D., Gunopulos, T., Hofmann, D., Malerba, & M., Vazirgiannis (Eds.), ECML PKDD 2011, Part III (pp. 113-128). Berlin, Heidelberg: Springer-Verlag.
Levels, J. H., Geurts, P., Karlsson, H., Marée, R., Ljunggren, S., Fornander, L., Wehenkel, L., Lindahl, M., Stroes, E. S., Kuivenhoven, J. A., & Meijers, J. C. (2011). High-density lipoprotein proteome dynamics in human endotoxemia. Proteome science, 9(1), 34.
Geurts, P. (2011). Learning from positive and unlabeled examples by enforcing statistical significance. JMLR: Workshop and Conference Proceedings, 15.
Geurts, P., & Louppe, G. (2011). Learning to rank with extremely randomized trees. JMLR: Workshop and Conference Proceedings, 14, 49-61.
Garbacki, N., Di Valentin, E., Huynh-Thu, V. A., Geurts, P., Irrthum, A., Crahay, C., Arnould, T., Deroanne, C., Piette, J., Cataldo, D., & Colige, A. (2011). MicroRNAs Profiling in Murine Models of Acute and Chronic Asthma: A Relationship with mRNAs Targets. PLoS ONE.
Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (2011). Automatic localization of interest points in zebrafish images with tree-based methods. Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics. Springer.

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.
Marée, R., Stern, O., & Geurts, P. (2010). Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments. CLEF Notebook Papers/LABs/Workshops.

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). Deriving p-values for tree-based variable importance measures. In L., Wehenkel, P., Geurts, & R., Marée (Eds.), Proceedings of Benelearn08.
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.
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.
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.

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.
Huynh-Thu, V. A., Hiard, S., Geurts, P., Muller, M., Struman, I., Martial, J., & Wehenkel, L. (2007, September). Detection of micro-RNA/gene interactions involved in angiogenesis using machine learning techniques. Paper presented at Workshop on Machine Learning in Systems Biology (MLSB07), Evry, France.
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), 1422-1433.

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: