Publications and communications of Pierre Geurts

Kumar, N., Claudia Di Biagio, Zachary Dellacqua, Raman, R., Arianna Martini, Clara Boglione, Muller, M., Geurts, P., & Marée, R. (In press). Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages. Paper presented at Bio Image Computing Workshop in European Conference on Computer Vision 2022, Tel Aviv, Israel.

Mormont, R., Testouri, M., Marée, R., & Geurts, P. (2022). Relieving pixel-wise labeling effort for pathology image segmentation with self-training. In Lecture Notes in Computer Science. Genève, Switzerland: Springer Cham.

Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2021). From global to local MDI variable importances for random forests and when they are Shapley values. Advances in Neural Information Processing Systems.

Sabatelli, M., & Geurts, P. (2021). On The Transferability of Deep-Q Networks. Deep Reinforcement Learning Workshop of the 35th Conference on Neural Information Processing Systems.

Navdeep Kumar, Zachary Dellacqua, Clara Boglione, Arianna Martini, Muller, M., Geurts, P., & Marée, R. (04 October 2021). Towards Setting up of an Automatic Recognition System for Vertebrae and Opercular Anomalies in Reared Gilthead Seabream(Sparus aurata). Poster session presented at Aquaculture Europe 2021, Funchal, Madeira, Portugal.

Kumar, N., Carletti, A., Gavaia, P. J., Muller, M., Cancela, L., Geurts, P., & Marée, R. (28 September 2021). Deep Learning approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images. Paper presented at International Conference on Computer Analysis of Images and Patterns.

Leroy, P., Ernst, D., Geurts, P., Louppe, G., Pisane, J., & Sabatelli, M. (2021). QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. In Proceedings of the AAAI-21 Workshop on Reinforcement Learning in Games (pp. 8).

Sabatelli, M., Banar, N., Cocriamont, M., Coudyzer, E., Lasaracina, K., Daelemans, W., Geurts, P., & Kestemont, M. (February 2021). Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digital Humanities Quarterly, 15 (1).

Sabatelli, M., Kestemont, M., & Geurts, P. (2021). On the Transferability of Winning Tickets in Non-Natural Image Datasets. 16th International Conference on Computer Vision Theory and Applications - VISAPP 2021.

Begon, J.-M., & Geurts, P. (2021). Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. IEEE Conference on Computer Vision and Pattern Recognition. Proceedings. doi:10.1109/CVPRW53098.2021.00367

Slavkov, I., Petkovic, M., Geurts, P., Kocev, D., & Dzeroski, S. (07 December 2020). Error curves for evaluating the quality of feature rankings. PeerJ Computer Science, 6 (e310), 39. doi:10.7717/peerj-cs.310

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2020). 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. doi:10.1109/JBHI.2020.2992878

Vecoven, N., Begon, J.-M., Sutera, A., Geurts, P., & Huynh-Thu, V. A. (2020). Nets versus trees for feature ranking and gene network inference. In Proceeding of the 23rd International Conference on Discovery Science (DS 2020). Springer. doi:10.1007/978-3-030-61527-7_16

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2019). 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. (October 2019). 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. (27 June 2019). A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes. Frontiers in Genetics, 10, 562. doi:10.3389/fgene.2019.00562

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, United States - New York: Humana Press. doi:10.1007/978-1-4939-8882-2_8

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. doi:10.1002/hbm.24701

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2018). Deep Quality Value (DQV) Learning. Advances in Neural Information Processing Systems.

Sabatelli, M., Kestemont, M., Daelemans, W., & Geurts, P. (2018). Deep Transfer Learning for Art Classification Problems. European Conference on Computer Vision (ECCV), 4th Workshop on Computer VISion for ART Analysis (VISART IV).

Wehenkel, M., Sutera, A., Bastin, C., Geurts, P.* , & Phillips, C.*. (29 June 2018). Random Forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Frontiers in Neuroscience, 12, 411. doi:10.3389/fnins.2018.00411
* These authors have contributed equally to this work.

Olivier, F., Sutera, A., Geurts, P., Fonteneau, R., & Ernst, D. (2018). Phase Identification of Smart Meters by Clustering Voltage Measurements. In Proceedings of the XX Power Systems Computation Conference (PSCC 2018). doi:10.23919/PSCC.2018.8442853

Wehenkel, M., Bastin, C., Geurts, P., & Phillips, C. (2018). Computer Aided Diagnosis System Based on Random Forests for the Prognosis of Alzheimer’s Disease. In 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science. Frontiers Media S.A. doi:10.3389/978-2-88945-421-1

Huynh-Thu, V. A., & Geurts, P. (21 February 2018). dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific Reports, 8, 3384. doi:10.1038/s41598-018-21715-0

Mormont, R., Geurts, P., & Marée, R. (2018). Comparison of deep transfer learning strategies for digital pathology. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE.

Pliakos, K., Geurts, P., & Vens, C. (2018). Global multi-output decision trees for interaction prediction. Machine Learning, 107, 1257-1281. doi:10.1007/s10994-018-5700-x

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, Spain: PMLR.

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. doi:10.1038/s41598-017-18993-5

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 https://orbi.uliege.be/2268/231719.

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. (December 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. doi:10.1186/s40635-017-0145-2

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. (09 October 2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14, 1083–1086. doi:10.1038/nmeth.4463

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. In Advanced Concepts for Intelligent Vision Systems (pp. 3-14). Springer. doi:10.1007/978-3-319-70353-4_1

Wehenkel, M., Bastin, C., Geurts, P., & Phillips, C. (28 June 2017). Parceling and tree-based ensemble methods for the prognosis of Alzheimer's disease. Poster session presented at 23rd Annual Meeting of the Organization for Human Brain Mapping, Vancouver, Canada.

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. doi:10.1007/978-3-319-53070-3

Wehenkel, M., Bastin, C., Phillips, C.* , & Geurts, P.*. (2017). Tree Ensemble Methods and Parcelling to Identify Brain Areas Related to Alzheimer’s Disease. In 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), proceedings. IEEE. doi:10.1109/PRNI.2017.7981513
* These authors have contributed equally to this work.

Wehenkel, M., Bastin, C., Phillips, C., & Geurts, P. (08 February 2017). Computer aided diagnosis system based on random forests for the prognosis of Alzheimer's disease. Paper presented at 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science, Vienne, Austria.

Begon, J.-M., Joly, A., & Geurts, P. (2017). Globally Induced Forest: A Prepruning Compression Scheme. Proceedings of Machine Learning Research, 70, 420-428.

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. In SCITEPRESS Digital Library.

Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Foidart, J.-M., Noël, A., & Maquoi, E. (October 2016). A membrane-type- matrix metalloproteinase (MT1-MMP) - discoidin domain receptor 1 axis regulates collagen-induced apoptosis in breast cancer cells. Poster session presented at EACR Meeting - Goodbye flat biology : models, mechanisms and microenvironment, Berlin, Germany.

Begon, J.-M., Joly, A., & Geurts, P. (12 September 2016). Joint learning and pruning of decision forests. Paper presented at The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.

Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (12 September 2016). Random subspace with trees for feature selection under memory constraints. Paper presented at The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.

Wehenkel, M., Geurts, P., & Phillips, C. (30 June 2016). Accuracy and interpretability, tree-based machine learning approaches. Poster session presented at 22nd Annual Meeting of the Organization for Human Brain Mapping, Geneva, Switzerland.

Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2016). Context-dependent feature analysis with random forests. In Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016).

Geurts, P., & Wehenkel, L. (19 April 2016). Comments on: A random forest guided tour. TEST, 25 (2), 247-253. doi:10.1007/s11749-016-0487-1

Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Noël, A., Foidart, J.-M., & Maquoi, E. (04 March 2016). A Membrane-Type-1 Matrix Metalloproteinase (MT1-MMP)- Discoïdin Domain Receptor 1 axis regulates collagen-induced apoptosis in breast cancer cells. Poster session presented at International Joint Meeting of the German and French Societies for Matrix Biology, Freiburg, Germany.

Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7. doi:10.1093/bioinformatics/btw013

Marée, R., Geurts, P., & Wehenkel, L. (2016). Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study. Pattern Recognition Letters, 74 (15), 17-23. doi:10.1016/j.patrec.2016.01.006

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). Circulating microRNA-based screening tool for breast cancer. Oncotarget. doi:10.18632/oncotarget.6786
* These authors have contributed equally to this work.

Delierneux, C., LAYIOS, N., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., GOTHOT, A., & Oury, C. (19 October 2015). Elevated basal levels of platelet-bound fibrinogen predict the occurrence of sepsis in ICU: a prospective study. Paper presented at Belgian Society on Thrombosis and Haemostasis (BSTH), Lamot, Belgium.

Du, W., Liao, Y., Tao, N., Geurts, P., Fu, X., & Leduc, G. (October 2015). Rating Network Paths for Locality-Aware Overlay Construction and Routing. IEEE/ACM Transactions on Networking, 23 (5), 1661-1673. doi:10.1109/TNET.2014.2337371

Liegeois, R., Ziegler, E., Bahri, M. A., Phillips, C., Geurts, P., Gomez, F., Yeo, T., VANHAUDENHUYSE, A., Soddu, A., LAUREYS, S., & Sepulchre, R. (2015). Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints. Brain Structure and Function. doi:10.1007/s00429-015-1083-y

Delierneux, C., LAYIOS, N., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., GOTHOT, A., & Oury, C. (23 June 2015). Prospective analysis of platelet activation markers to predict severe infection and mortality in intensive care units. Poster session presented at XXV Congress of the International Society on Thrombosis and Haemostasis (ISTH), Toronto, Canada.

Schrynemackers, M., Wehenkel, L., Madan Babu, M., & Geurts, P. (11 May 2015). Classifying pairs with trees for supervised biological network inference. Molecular Biosystems, 11 (8), 2116-2125. doi:10.1039/c5mb00174a

Vandaele, R., Marée, R., COUCKE, P., GULYBAN, A., LALLEMAND, F., Geurts, P., JODOGNE, S., & MARTINIVE, P. (April 2015). Automated Landmark Detection For Rigid Registration Between The Simulation-CT and the Treatment CBCT. Poster session presented at 3rd ESTRO FORUM, Barcelone, Spain.

Wehenkel, M., Geurts, P., & Phillips, C. (15 March 2015). Tree Ensemble Methods for Computer Aided Diagnosis (CAD) Systems. Poster session presented at 2nd HBP Education Workshop : Future Medicine, Lausanne, Switzerland.

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. doi:10.1371/journal.pone.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. doi:10.1109/TMI.2015.2412951

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. doi:10.1371/journal.pone.0116989

LAYIOS, N., Delierneux, C., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., Gothot, A., & Oury, C. (2015). Erratum: Elevated basal levels of circulating activated platelets predict ICU-acquired sepsis and mortality: a prospective study. Critical Care, 19 (1), 301. doi:10.1186/s13054-015-1005-7

LAYIOS, N., GOSSET, C., Delierneux, C., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., Oury, C., & Gothot, A. (2015). Erratum: Prospective immune profiling in critically ill adults: before, during and after severe sepsis and septic shock. Critical Care, 19 (1), 300. doi:10.1186/s13054-015-1006-6

Marée, R., Geurts, P., & Wehenkel, L. (2014). Towards generic image classification: an extensive empirical study. (1). Eprint/Working paper retrieved from https://orbi.uliege.be/2268/175525.

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. (18 December 2014). Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference. Cell Reports, 9 (6), 2290-2303. doi:10.1016/j.celrep.2014.11.038

Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. In Machine Learning and Knowledge Discovery in Databases. doi:10.1007/978-3-662-44848-9_39

Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2014). 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. Springer.

Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE. doi:10.1371/journal.pone.0093379

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. In 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. (25 March 2014). NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms. PLoS ONE, 9 (3), 92709. doi:10.1371/journal.pone.0092709

Schepers, K., Mouchet, F., Dirix, V., De Schutter, I., Kersten, J., Verscheure, V., Geurts, P., Mahavir, S., Van Vooren, J.-P., & Mascart, F. (February 2014). 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. doi:10.1128/CVI.00525-13

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. doi:10.1152/ajpgi.00484.2012

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. New Phytologist, 203 (2), 685-696. doi:10.1111/nph.12818

Vandaele, R., Marée, R., JODOGNE, S., & Geurts, P. (2014). Automatic Landmark Detection in 2D images: A tree-based approach with multiresolution pixel features. ULG.

Vandaele, R., Marée, R., JODOGNE, S., & Geurts, P. (2014). Automatic Cephalometric X-Ray Landmark Detection Challenge 2014: A tree-based algorithm. ISBI.

Schrynemackers, M., Kuffner, R., & Geurts, P. (03 December 2013). On protocols and measures for the validation of supervised methods for the inference of biological networks. Frontiers in Genetics, 4 (262). doi:10.3389/fgene.2013.00262

Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Advances in Neural Information Processing Systems 26.

Liao, Y., Du, W., Geurts, P., & Leduc, G. (11 October 2013). DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. IEEE/ACM Transactions on Networking, 21 (5), 1511-1524. doi:10.1109/TNET.2012.2228881

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. (August 2013). Automated Processing of Zebrafish Imaging Data: A Survey. Zebrafish, 10 (3), 401-421. doi:10.1089/zeb.2013.0886

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.

Huynh-Thu, V. A., & Geurts, P. (2013). Gene regulatory network inference from expression and genetic data using tree-based methods. Paper presented at STATSEQ meeting on Gene Network Inference with Systems genetic data and beyond, Paris, France.

Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2013). Gene regulatory network inference from systems genetics data using tree-based methods. In A. de la Fuente (Ed.), Gene Network Inference - Verification of Methods for Systems Genetics Data (pp. 63-85). Springer. doi:10.1007/978-3-642-45161-4_5

Du, W., Liao, Y., Geurts, P., & Leduc, G. (2012). Ordinal Rating of Network Performance and Inference by Matrix Completion. (arXiv:1211.0447).

Maes, F., Geurts, P., & Wehenkel, L. (2012). Embedding Monte Carlo search of features in tree-based ensemble methods. In P. Flach, T. De Bie, ... N. Cristianini (Eds.), Machine Learning and Knowledge Discovery in Data Bases (pp. 191-206). Springer.

Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). Mixtures of Bagged Markov Tree Ensembles. In A. Cano Utrera, M. Gómez-Olmedo, ... T. Nielsen (Eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (pp. 283-290).

Hiard, S., Geurts, P., & Wehenkel, L. (2012). Comparator selection for RPC with many labels. In ECAI 2012 : 20th European Conference on Artificial Intelligence : 27-31 August 2012, Montpellier, France (pp. 408-413). Amsterdam, Netherlands: IOS Press. doi:10.3233/978-1-61499-098-7-408

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., ... Stolovitzky, G. (15 July 2012). Wisdom of crowds for robust gene network inference. Nature Methods, 9, 796-804. doi:10.1038/nmeth.2016

Huynh-Thu, V. A., Vandel, J., Irrthum, A., Wehenkel, L., & Geurts, P. (01 June 2012). Inferring gene regulatory networks from genetical genomics data. Paper presented at Capita Selecta in Complex Disease Analysis, Liege, Belgium.

Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. In Proceeding of the 21st Belgian-Dutch Conference on Machine Learning.

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).

Huynh-Thu, V. A., Saeys, Y., Wehenkel, L., & Geurts, P. (25 April 2012). Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics, 28 (13), 1766-1774. doi:10.1093/bioinformatics/bts238

Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. In 20th European Symposium on Artificial Neural Networks.

Liao, Y., Du, W., Geurts, P., & Leduc, G. (2012). DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. (arXiv:1201.1174).

Louppe, G., & Geurts, P. (2012). Ensembles on Random Patches. In Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer-Verlag.

Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (08 December 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning. Poster session presented at BelTox Annual Scientific Meeting 2011, Mechelen, Belgium.

Liao, Y., Du, W., Geurts, P., & Leduc, G. (2011). Decentralized Prediction of End-to-End Network Performance Classes. In Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT). ACM. doi:10.1145/2079296.2079310

Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (29 November 2011). Pruning randomized trees with L1-norm regularization. Poster session presented at DYSCO Study Day, Leuven-Heverlee, Belgium.

Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (02 September 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning. Paper presented at MIAAB 2011 - Microscopic Image Analysis with Applications in Biology, Heidelberg, Germany.

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.), Machine Learning and Knowledge Discovery in Databases, Part III (pp. 113-128). Berlin, Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-23808-6_8

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. (28 June 2011). High-density lipoprotein proteome dynamics in human endotoxemia. Proteome Science, 9 (1), 34. doi:10.1186/1477-5956-9-34

Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (20 May 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning. Poster session presented at Benelearn 2011 - 20th Annual Belgian Dutch Conference on Machine Learning, La Haye, Netherlands.

Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (20 May 2011). Zebrafish Skeleton Measurements using Image Analysis and Machine Learning Methods. Poster session presented at Belgian Dutch Conference on Machine learning (Benelearn).

Geurts, P. (April 2011). Learning from positive and unlabeled examples by enforcing statistical significance. Proceedings of Machine Learning Research, 15.

Schnitzler, F., Geurts, P., & Wehenkel, L. (21 March 2011). Looking for applications of mixtures of Markov trees in bioinformatics. Paper presented at BioMAGNet Annual Meeting 2011, Bruxelles, Belgium.

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. doi:10.1371/journal.pone.0016509

Geurts, P., & Louppe, G. (January 2011). Learning to rank with extremely randomized trees. Proceedings of Machine Learning Research, 14, 49-61.

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., Saeys, Y., & Geurts, P. (2011). Inferring gene regulatory networks from expression data using tree-based methods. Paper presented at ISMB/ECCB 2011, Vienna, Austria.

Huynh-Thu, V. A., Saeys, Y., Wehenkel, L., & Geurts, P. (2011). Statistical interpretation of machine learning-based feature rankings for biomarker discovery. Paper presented at Benelux Bioinformatics Conference (BBC11), Luxembourg, Luxembourg.

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. In Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics. Springer.

Louppe, G., & Geurts, P. (11 December 2010). A zealous parallel gradient descent algorithm. Poster session presented at NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, Whistler, Canada.

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., Saeys, Y., & Geurts, P. (16 November 2010). Regulatory network inference with GENIE3: application to the DREAM5 challenge. Paper presented at 3rd Annual Joint Conference on Systems Biology, Regulatory Genomics, and Reverse Engineering Challenges, New York City, United States - New York.

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (October 2010). Inferring Regulatory Networks from Expression Data using Tree-based Methods. Paper presented at Workshop on Machine Learning in Systems Biology (MLSB10), Edinburgh, United Kingdom.

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (28 September 2010). Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE, 5 (9), 12776. doi:10.1371/journal.pone.0012776

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (September 2010). Inferring regulatory networks from expression data using tree-based methods. Poster session presented at ECCB 2010, Ghent, Belgium.

Liao, Y., Geurts, P., & Leduc, G. (11 May 2010). Network Distance Prediction Based on Decentralized Matrix Factorization. Lecture Notes in Computer Science, 6091, 15-26. doi:10.1007/978-3-642-12963-6_2

Marée, R., Denis, P., Wehenkel, L., & Geurts, P. (2010). Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies. In ACM Proceedings MIR 2010. doi:10.1145/1743384.1743405

El Khayat, I., Geurts, P., & Leduc, G. (February 2010). Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wireless Networks, 16 (2), 273-290. doi:10.1007/s11276-008-0129-y

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, 18 (1127), 1132. doi:10.1038/ejhg.2010.48

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. In CLEF 2010: Padua, Italy - Notebook Papers/LABs/Workshops.

Cornélusse, B., Geurts, P., & Wehenkel, L. (12 December 2009). 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. (December 2009). Supervised learning with decision tree-based methods in computational and systems biology. Molecular Biosystems, 5 (12), 1593-1605. doi:10.1039/b907946g

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (September 2009). Inferring regulatory networks from expression data using tree-based methods. Poster session presented at Benelux Bioinformatics Conference (BBC09), Liège, Belgium.

Liao, Y., Kaafar, M. A., Gueye, B., Cantin, F., Geurts, P., & Leduc, G. (12 May 2009). Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning. Lecture Notes in Computer Science, 5550, 352-363. doi:10.1007/978-3-642-01399-7_28

Dumont, M., Marée, R., Wehenkel, L., & Geurts, P. (2009). Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. In Proc. International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 196-203).

Marée, R., Geurts, P., & Wehenkel, L. (30 January 2009). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. IPSJ Transactions on Computer Vision and Applications, 1. doi:10.2197/ipsjtcva.1.46

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.

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2009). Inferring regulatory networks from expression data using tree-based methods. Paper presented at 2009 joint conference on Systems Biology, Regulatory Genomics, and Reverse Engineering Challenges, Boston, United States - Massachusetts.

Marée, R., Stevens, B., Geurts, P., Guern, Y., & Mack, P. (2009). A Machine Learning Approach for Material Detection in Hyperspectral Images. In Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09). IEEE. doi:10.1109/CVPR.2009.5204119

Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (December 2008). Exploiting tree-based variable importances to selectively identify relevant variables. Poster session presented at Benelux Bioinformatics Conference (BBC08), Maastricht, Netherlands.

Botta, V., Hansoul, S., Geurts, P., & Wehenkel, L. (2008). Raw genotypes vs haplotype blocks for genome wide association studies by random forests. In Proc. of MLSB 2008, second workshop on Machine Learning in Systems Biology.

Botta, V., Geurts, P., Hansoul, S., & Wehenkel, L. (May 2008). Prediction of genetic risk of complex diseases by supervised learning. Paper presented at Benelearn: The annual machine learning conference of Belgium and The Netherlands.

Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (May 2008). Deriving p-values for tree-based variable importance measures. Paper presented at Benelearn, Spa, Belgium.

Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. Proceedings of Machine Learning Research, 4, 60-73.

Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. Paper presented at Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery (FSDM 2008).

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. Poster session 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 (12), 960-7. doi:10.1016/j.clinbiochem.2008.04.021
* These authors have contributed equally to this work.

Del Angel, A., Geurts, P., Ernst, D., Glavic, M., & Wehenkel, L. (October 2007). Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing, 70 (16-18), 2668-2678. doi:10.1016/j.neucom.2006.12.017

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

Marée, R., Dumont, M., Geurts, P., & Wehenkel, L. (22 July 2007). Random Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation. Poster session 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. (July 2007). Machine-learnt versus analytical models of TCP throughput. Computer Networks, 51 (10), 2631-2644. doi:10.1016/j.comnet.2006.11.017

Geurts, P., Touleimat, N., Dutreix, M., & d'Alche-Buc, F. (03 May 2007). Inferring biological networks with output kernel trees. BMC Bioinformatics, 8 (Suppl. 2), 4. doi:10.1186/1471-2105-8-S2-S4

Dumont, M., Marée, R., Geurts, P., & Wehenkel, L. (2007). Random Subwindows and Multiple Output Decision Trees for Generic Image Annotation. Poster session presented at 16th Annual Machine Learning Conference of Belgium and The Netherlands, Amsterdam, Netherlands.

Geurts, P., Wehenkel, L., & d'Alché-Buc, F. (2007). Gradient boosting for kernelized output spaces. In Proceedings of the 24th International Conference on Machine Learning (pp. 289-296). doi:10.1145/1273496.1273533

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). doi:10.1186/1471-2121-8-S1-S2

Marée, R., Geurts, P., & Wehenkel, L. (2007). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. In Proc. 8th Asian Conference on Computer Vision (ACCV), LNCS (pp. 611-620). Springer-Verlag.

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. doi:10.1016/j.bcp.2006.12.019
* These authors have contributed equally to this work.

El Khayat, I., Geurts, P., & Leduc, G. (May 2006). On the accuracy of analytical models of TCP throughput. Lecture Notes in Computer Science, 3976, 488-500. doi:10.1007/11753810_41

Geurts, P., Ernst, D., & Wehenkel, L. (April 2006). Extremely randomized trees. Machine Learning, 63 (1), 3-42. doi:10.1007/s10994-006-6226-1

Auvray, V., Geurts, P., & Wehenkel, L. (2006). A Semi-Algebraic Description of Discrete Naive Bayes Models with Two Hidden Classes. In 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. In 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. In Proceedings of the the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology.

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. In Proc. of CAP (Conférence francophone d'apprentissage) (pp. 16).

Geurts, P., Wehenkel, L., & d Alché-Buc, F. (2006). Kernelizing the output of tree-based methods. In Proceedings of the 23rd International Conference on Machine Learning (pp. 345--352). Acm.

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, United States.

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. In 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. In Proceedings of Robust Methods for Power System State Estimation and Load Forecasting.

Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). Automatic learning of sequential decision strategies for dynamic security assessment and control. In Proceedings of the IEEE Power Engineering Society General Meeting 2006. doi:10.1109/PES.2006.1708874

Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). About automatic learning for advanced sensing, monitoring and control of electric power systems. In Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry.

El Khayat, I., Geurts, P., & Leduc, G. (May 2005). Improving TCP in wireless networks with an adaptive machine-learnt classifier of packet loss causes. Lecture Notes in Computer Science, 3462, 549-560. doi:10.1007/11422778_44

Ernst, D., Geurts, P., & Wehenkel, L. (April 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. doi:10.1002/art.21607
* These authors have contributed equally to this work.

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). doi:10.2202/1553-779X.1066

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. In Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005) (pp. 194--201). doi:10.1109/cibcb.2005.1594917

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. doi:10.1093/bioinformatics/bti494

Geurts, P., & Wehenkel, L. (2005). Segment and combine approach for non-parametric time-series classification. Lecture Notes in Computer Science, 3721, 478-485. doi:10.1007/11564126_48

Geurts, P., & Wehenkel, L. (2005). Closed-form dual perturb and combine for tree-based models. In Proceedings of the International Conference on Machine Learning (ICML 2005). doi:10.1145/1102351.1102381

Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Random Subwindows for Robust Image Classification. In C. Schmid, S. Soatto, ... C. Tomasi (Eds.), Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (pp. 34--40).

Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Decision Trees and Random Subwindows for Object Recognition. In ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005).

Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Biomedical image classification with random subwindows and decision trees. In Computer Vision for Biomedical Image Applications (pp. 220-229). Berlin, Germany: Springer-Verlag Berlin. doi:10.1007/11569541_23

Geurts, P., El Khayat, I., & Leduc, G. (2004). A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks. Paper presented at ICDM 2004, Brighton, United Kingdom. doi:10.1109/ICDM.2004.10063

De Seny, D., Fillet, M., Meuwis, M.-A., Lutteri, L., Geurts, P., Wehenkel, L., Bours, V., Piette, J., Malaise, M., & Merville, M.-P. (September 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. (2004). A generic approach for image classification based on decision tree ensembles and local sub-windows. In K.-S. Hong & Z. Zhang (Eds.), Proceedings of the 6th Asian Conference on Computer Vision (pp. 860-865). Asian Federation of Computer Vision Societies (AFCV).

Ernst, D., Geurts, P., & Wehenkel, L. (2003). Iteratively extending time horizon reinforcement learning. In Machine Learning: ECML 2003, 14th European Conference on Machine Learning (pp. 96-107). Berlin, Germany: Springer-Verlag Berlin. doi:10.1007/978-3-540-39857-8_11

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. (2003). An empirical comparison of machine learning algorithms for generic image classification. In F. Coenen, A. Preece, ... A. L. Macintosh (Eds.), 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.

Geurts, P. (2002). Contributions to decision tree induction: bias/variance tradeoff and time series classification. Unpublished doctoral thesis, ULiège - Université de Liège.
Jury: Wehenkel, L. (Promotor).

Geurts, P. (2001). Pattern extraction for time-series classification. In Proceedings of PKDD 2001, 5th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 115-127). Springer-Verlag.

Geurts, P. (2001). Dual Perturb and Combine Algorithm. In Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics (pp. 196-201).

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.

Geurts, P. (2000). Some enhancements of decision tree bagging. In 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). Temporal machine learning for switching control. In Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 401-408). Lyon, France: Springer-Verlag.

Geurts, P., & Wehenkel, L. (2000). Investigation and reduction of discretization Variance in decision tree induction. In Proceedings of ECML 2000, European Conference on Machine Learning (pp. 162-170). Springer-Verlag.

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

Geurts, P., & Wehenkel, L. (1998). Visualizing dynamic power system scenarios for data mining. In Proceedings of LESCOPE 98, Large Engineering Syst. Conf. on Power Engineering (pp. 217-224).

Geurts, P., & Wehenkel, L. (1998). Early prediction of electric power system blackouts by temporal machine learning. In Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis" (pp. 21-27).