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ORCIDPierre Geurts
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- affiliation: Université de Liège, Belgium
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Books and Theses
- 2002
[b1]Pierre Geurts:
Contributions to decision tree induction: bias/variance tradeoff and time series classification. University of Liège, Belgium, 2002
Journal Articles
- 2024
- [j20]Amir Hossein Akhavan Rahnama
, Judith Bütepage, Pierre Geurts, Henrik Boström:
Can local explanation techniques explain linear additive models? Data Min. Knowl. Discov. 38(1): 237-280 (2024) - [j19]Vân Anh Huynh-Thu, Pierre Geurts:
Optimizing model-agnostic random subspace ensembles. Mach. Learn. 113(2): 993-1042 (2024) - 2021
- [j18]Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts, Mike Kestemont:
Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digit. Humanit. Q. 15(1) (2021) - [j17]Romain Mormont, Pierre Geurts, Raphaël Marée:
Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology. IEEE J. Biomed. Health Informatics 25(2): 412-421 (2021) - 2020
- [j16]Ivica Slavkov, Matej Petkovic, Pierre Geurts, Dragi Kocev, Saso Dzeroski:
Error curves for evaluating the quality of feature rankings. PeerJ Comput. Sci. 6: e310 (2020) - 2018
- [j15]Konstantinos Pliakos, Pierre Geurts, Celine Vens:
Global multi-output decision trees for interaction prediction. Mach. Learn. 107(8-10): 1257-1281 (2018) - 2016
- [j14]Raphaël Marée, Loic Rollus, Benjamin Stevens, Renaud Hoyoux, Gilles Louppe, Remy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts, Louis Wehenkel:
Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinform. 32(9): 1395-1401 (2016) - [j13]Raphaël Marée, Pierre Geurts, Louis Wehenkel:
Towards generic image classification using tree-based learning: An extensive empirical study. Pattern Recognit. Lett. 74: 17-23 (2016) - 2015
- [j12]Ching-Wei Wang, Cheng-Ta Huang, Meng-Che Hsieh, Chung-Hsing Li, Sheng-Wei Chang, Wei-Cheng Li, Remy Vandaele, Raphaël Marée, Sébastien Jodogne, Pierre Geurts, Cheng Chen, Guoyan Zheng, Chengwen Chu, Hengameh Mirzaalian, Ghassan Hamarneh, Tomaz Vrtovec, Bulat Ibragimov:
Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans. Medical Imaging 34(9): 1890-1900 (2015) - [j11]Wei Du, Yongjun Liao, Narisu Tao, Pierre Geurts, Xiaoming Fu, Guy Leduc:
Rating Network Paths for Locality-Aware Overlay Construction and Routing. IEEE/ACM Trans. Netw. 23(5): 1661-1673 (2015) - 2013
- [j10]Yongjun Liao, Wei Du, Pierre Geurts, Guy Leduc:
DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. IEEE/ACM Trans. Netw. 21(5): 1511-1524 (2013) - 2012
- [j9]Vân Anh Huynh-Thu, Yvan Saeys, Louis Wehenkel, Pierre Geurts:
Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinform. 28(13): 1766-1774 (2012) - 2010
- [j8]Ibtissam El Khayat, Pierre Geurts, Guy Leduc:
Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wirel. Networks 16(2): 273-290 (2010) - 2009
- [j7]Raphaël Marée, Pierre Geurts, Louis Wehenkel:
Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. IPSJ Trans. Comput. Vis. Appl. 1: 46-57 (2009) - 2007
- [j6]Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc:
Inferring biological networks with output kernel trees. BMC Bioinform. 8(S-2) (2007) - [j5]Ibtissam El Khayat, Pierre Geurts, Guy Leduc:
Machine-learnt versus analytical models of TCP throughput. Comput. Networks 51(10): 2631-2644 (2007) - [j4]Alberto Del Angel, Pierre Geurts, Damien Ernst, Mevludin Glavic, Louis Wehenkel:
Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing 70(16-18): 2668-2678 (2007) - 2006
- [j3]Pierre Geurts, Damien Ernst, Louis Wehenkel:
Extremely randomized trees. Mach. Learn. 63(1): 3-42 (2006) - 2005
- [j2]Pierre Geurts, Marianne Fillet, Dominique de Seny, Marie-Alice Meuwis, Michel Malaise, Marie-Paule Merville, Louis Wehenkel:
Proteomic mass spectra classification using decision tree based ensemble methods. Bioinform. 21(14): 3138-3145 (2005) - [j1]Damien Ernst, Pierre Geurts, Louis Wehenkel:
Tree-Based Batch Mode Reinforcement Learning. J. Mach. Learn. Res. 6: 503-556 (2005)
Conference and Workshop Papers
- 2023
- [c60]Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts:
Knowledge-Guided Additive Modeling for Supervised Regression. DS 2023: 64-78 - 2022
- [c59]Navdeep Kumar, Claudia Di Biagio, Zachary Dellacqua, Ratish Raman, Arianna Martini, Clara Boglione, Marc Muller, Pierre Geurts, Raphaël Marée:
Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages. ECCV Workshops (4) 2022: 470-486 - [c58]Romain Mormont, Mehdi Testouri, Raphaël Marée, Pierre Geurts:
Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-training. ECCV Workshops (7) 2022: 577-592 - 2021
- [c57]Navdeep Kumar, Alessio Carletti, Paulo J. Gavaia, Marc Muller, M. Leonor Cancela, Pierre Geurts, Raphaël Marée:
Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images. CAIP (1) 2021: 154-164 - [c56]Jean-Michel Begon, Pierre Geurts:
Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. CVPR Workshops 2021: 3290-3299 - [c55]Nikolay Banar, Matthia Sabatelli, Pierre Geurts, Walter Daelemans, Mike Kestemont:
Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain. Computer Vision and Image Analysis of Art 2021: 1-9 - [c54]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
From global to local MDI variable importances for random forests and when they are Shapley values. NeurIPS 2021: 3533-3543 - [c53]Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-natural Image Datasets. VISIGRAPP (5: VISAPP) 2021: 59-69 - 2020
- [c52]Nicolas Vecoven, Jean-Michel Begon, Antonio Sutera, Pierre Geurts, Vân Anh Huynh-Thu:
Nets Versus Trees for Feature Ranking and Gene Network Inference. DS 2020: 231-245 - [c51]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN 2020: 1-8 - 2019
- [c50]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN 2019 - 2018
- [c49]Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. AISTATS 2018: 929-937 - [c48]Romain Mormont, Pierre Geurts, Raphaël Marée:
Comparison of Deep Transfer Learning Strategies for Digital Pathology. CVPR Workshops 2018: 2262-2271 - [c47]Matthia Sabatelli, Mike Kestemont, Walter Daelemans, Pierre Geurts:
Deep Transfer Learning for Art Classification Problems. ECCV Workshops (2) 2018: 631-646 - 2017
- [c46]Samir Azrour, Sébastien Piérard, Pierre Geurts, Marc Van Droogenbroeck:
A Two-Step Methodology for Human Pose Estimation Increasing the Accuracy and Reducing the Amount of Learning Samples Dramatically. ACIVS 2017: 3-14 - [c45]Jean-Michel Begon, Arnaud Joly, Pierre Geurts:
Globally Induced Forest: A Prepruning Compression Scheme. ICML 2017: 420-428 - [c44]M. Wehenkel, Christine Bastin, Christophe Phillips, Pierre Geurts:
Tree ensemble methods and parcelling to identify brain areas related to Alzheimer's disease. PRNI 2017: 1-4 - [c43]Remy Vandaele, François Lallemand, Philippe Martinive, Akos Gulyban, Sébastien Jodogne, Philippe Coucke, Pierre Geurts, Raphaël Marée:
Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection. VISIGRAPP (5: VISAPP) 2017: 333-340 - 2016
- [c42]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Context-dependent feature analysis with random forests. UAI 2016 - 2014
- [c41]Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts:
Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging. Neural Connectomics 2014: 23-34 - [c40]Samir Azrour, Sébastien Piérard, Pierre Geurts, Marc Van Droogenbroeck:
Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. ESANN 2014 - [c39]Arnaud Joly, Pierre Geurts, Louis Wehenkel:
Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification. ECML/PKDD (1) 2014: 607-622 - 2013
- [c38]Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts:
Understanding variable importances in forests of randomized trees. NIPS 2013: 431-439 - 2012
- [c37]Samuel Hiard, Pierre Geurts, Louis Wehenkel:
Comparator selection for RPC with many labels. ECAI 2012: 408-413 - [c36]Arnaud Joly, François Schnitzler, Pierre Geurts, Louis Wehenkel:
L1-based compression of random forest models. ESANN 2012 - [c35]Francis Maes, Pierre Geurts, Louis Wehenkel:
Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods. ECML/PKDD (1) 2012: 191-206 - [c34]Gilles Louppe, Pierre Geurts:
Ensembles on Random Patches. ECML/PKDD (1) 2012: 346-361 - 2011
- [c33]Yongjun Liao, Wei Du, Pierre Geurts, Guy Leduc:
Decentralized prediction of end-to-end network performance classes. CoNEXT 2011: 14 - [c32]François Schnitzler, Sourour Ammar, Philippe Leray, Pierre Geurts, Louis Wehenkel:
Efficiently Approximating Markov Tree Bagging for High-Dimensional Density Estimation. ECML/PKDD (3) 2011: 113-128 - [c31]Olivier Stern, Raphaël Marée, Jessica Aceto, Nathalie Jeanray, Marc Muller, Louis Wehenkel, Pierre Geurts:
Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods. PRIB 2011: 179-190 - [c30]Pierre Geurts, Gilles Louppe:
Learning to rank with extremely randomized trees. Yahoo! Learning to Rank Challenge 2011: 49-61 - [c29]Pierre Geurts:
Learning from positive and unlabeled examples by enforcing statistical significance. AISTATS 2011: 305-314 - 2010
- [c28]Raphaël Marée, Olivier Stern, Pierre Geurts:
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) 2010 - [c27]Raphaël Marée, Philippe Denis, Louis Wehenkel, Pierre Geurts:
Incremental indexing and distributed image search using shared randomized vocabularies. Multimedia Information Retrieval 2010: 91-100 - [c26]Yongjun Liao, Pierre Geurts, Guy Leduc:
Network Distance Prediction Based on Decentralized Matrix Factorization. Networking 2010: 15-26 - [c25]Saso Dzeroski, Pierre Geurts, Juho Rousu:
Preface. MLSB 2010: 1-2 - 2009
- [c24]Raphaël Marée, Benjamin Stevens, Pierre Geurts, Y. Guern, P. Mack:
A machine learning approach for material detection in hyperspectral images. CVPR Workshops 2009: 106-111 - [c23]Yongjun Liao, Mohamed Ali Kâafar, Bamba Gueye, François Cantin, Pierre Geurts, Guy Leduc:
Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning. Networking 2009: 352-363 - [c22]Marie Dumont, Raphaël Marée, Louis Wehenkel, Pierre Geurts:
Fast Multi-class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. VISAPP (2) 2009: 196-203 - 2008
- [c21]Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Exploiting tree-based variable importances to selectively identify relevant variables. FSDM 2008: 60-73 - 2007
- [c20]Raphaël Marée, Pierre Geurts, Louis Wehenkel:
Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees. ACCV (2) 2007: 611-620 - [c19]Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
Gradient boosting for kernelized output spaces. ICML 2007: 289-296 - 2006
- [c18]Minh Quach, Pierre Geurts:
Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data. ESANN 2006: 569-574 - [c17]Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
Kernelizing the output of tree-based methods. ICML 2006: 345-352 - [c16]Vincent Auvray, Pierre Geurts, Louis Wehenkel:
A Semi-Algebraic Description of Naive Bayes Models with Two Hidden Classes. AI&M 2006 - [c15]Ibtissam El Khayat, Pierre Geurts, Guy Leduc:
On the Accuracy of Analytical Models of TCP Throughput. Networking 2006: 488-500 - 2005
- [c14]Pierre Geurts, Antia Blanco Cuesta, Louis Wehenkel:
Segment and Combine Approach for Biological Sequence Classification. CIBCB 2005: 194-201 - [c13]Raphaël Marée, Pierre Geurts, Justus H. Piater, Louis Wehenkel:
Biomedical Image Classification with Random Subwindows and Decision Trees. CVBIA 2005: 220-229 - [c12]Raphaël Marée, Pierre Geurts, Justus H. Piater, Louis Wehenkel:
Random Subwindows for Robust Image Classification. CVPR (1) 2005: 34-40 - [c11]Pierre Geurts, Louis Wehenkel:
Closed-form dual perturb and combine for tree-based models. ICML 2005: 233-240 - [c10]Ibtissam El Khayat, Pierre Geurts, Guy Leduc:
Improving TCP in Wireless Networks with an Adaptive Machine-Learnt Classifier of Packet Loss Causes. NETWORKING 2005: 549-560 - [c9]Pierre Geurts, Louis Wehenkel:
Segment and Combine Approach for Non-parametric Time-Series Classification. PKDD 2005: 478-485 - 2004
- [c8]Pierre Geurts, Ibtissam El Khayat, Guy Leduc:
A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks. ICDM 2004: 383-386 - 2003
- [c7]Damien Ernst, Pierre Geurts, Louis Wehenkel:
Iteratively Extending Time Horizon Reinforcement Learning. ECML 2003: 96-107 - [c6]Raphaël Marée, Pierre Geurts, Giorgio Visimberga, Justus H. Piater, Louis Wehenkel:
A Comparison of Generic Machine Learning Algorithms for Image Classification. SGAI Conf. 2003: 169-182 - 2001
- [c5]Pierre Geurts:
Dual perturb and combine algorithm. AISTATS 2001: 106-111 - [c4]Pierre Geurts:
Pattern Extraction for Time Series Classification. PKDD 2001: 115-127 - 2000
- [c3]Pierre Geurts, Louis Wehenkel:
Investigation and Reduction of Discretization Variance in Decision Tree Induction. ECML 2000: 162-170 - [c2]Pierre Geurts:
Some Enhencements of Decision Tree Bagging. PKDD 2000: 136-147 - [c1]Pierre Geurts, Louis Wehenkel:
Temporal Machine Learning for Switching Control. PKDD 2000: 401-408
Parts in Books or Collections
- 2010
- [p2]Pierre Geurts:
Bias vs Variance Decomposition for Regression and Classification. Data Mining and Knowledge Discovery Handbook 2010: 733-746 - 2005
- [p1]Pierre Geurts:
Bias vs. Variance Decomposition for Regression and Classification. The Data Mining and Knowledge Discovery Handbook 2005: 749-763
Editorship
- 2020
- [e3]Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe:
Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers. Communications in Computer and Information Science 1196, Springer 2020, ISBN 978-3-030-65153-4 [contents] - 2019
- [e2]Katrien Beuls, Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe, Paul Van Eecke:
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. CEUR Workshop Proceedings 2491, CEUR-WS.org 2019 [contents] - 2010
- [e1]Saso Dzeroski, Pierre Geurts, Juho Rousu:
Proceedings of the third International Workshop on Machine Learning in Systems Biology, MLSB 2009, Ljubljana, Slovenia, September 5-6, 2009. JMLR Proceedings 8, JMLR.org 2010 [contents]
Informal and Other Publications
- 2024
- [i20]Gaspard Lambrechts, Yann Claes, Pierre Geurts, Damien Ernst:
Parallelizing Autoregressive Generation with Variational State Space Models. CoRR abs/2407.08415 (2024) - 2023
- [i19]Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts:
Knowledge-Guided Additive Modeling For Supervised Regression. CoRR abs/2307.02229 (2023) - 2022
- [i18]Jean-Michel Begon, Pierre Geurts:
Distillation from heterogeneous unlabeled collections. CoRR abs/2201.06507 (2022) - 2021
- [i17]Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström:
Evaluation of Local Model-Agnostic Explanations Using Ground Truth. CoRR abs/2106.02488 (2021) - [i16]Vân Anh Huynh-Thu, Pierre Geurts:
Optimizing model-agnostic Random Subspace ensembles. CoRR abs/2109.03099 (2021) - [i15]Matthia Sabatelli, Pierre Geurts:
On The Transferability of Deep-Q Networks. CoRR abs/2110.02639 (2021) - [i14]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
From global to local MDI variable importances for random forests and when they are Shapley values. CoRR abs/2111.02218 (2021) - 2020
- [i13]Romain Mormont, Pierre Geurts, Raphaël Marée:
Multi-task pre-training of deep neural networks for digital pathology. CoRR abs/2005.02561 (2020) - [i12]Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-Natural Image Datasets. CoRR abs/2005.05232 (2020) - [i11]Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli:
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2012.12062 (2020) - 2019
- [i10]Arnaud Joly, Louis Wehenkel, Pierre Geurts:
Gradient tree boosting with random output projections for multi-label classification and multi-output regression. CoRR abs/1905.07558 (2019) - [i9]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. CoRR abs/1909.01779 (2019) - 2018
- [i8]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. CoRR abs/1810.00368 (2018) - 2017
- [i7]Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. CoRR abs/1709.01177 (2017) - 2016
- [i6]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Context-dependent feature analysis with random forests. CoRR abs/1605.03848 (2016) - 2014
- [i5]Arnaud Joly, Pierre Geurts, Louis Wehenkel:
Random forests with random projections of the output space for high dimensional multi-label classification. CoRR abs/1404.3581 (2014) - [i4]Marie Schrynemackers, Louis Wehenkel, M. Madan Babu, Pierre Geurts:
Classifying pairs with trees for supervised biological network inference. CoRR abs/1404.6074 (2014) - [i3]Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts:
Simple connectome inference from partial correlation statistics in calcium imaging. CoRR abs/1406.7865 (2014) - 2012
- [i2]Yongjun Liao, Wei Du, Pierre Geurts, Guy Leduc:
DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. CoRR abs/1201.1174 (2012) - [i1]Wei Du, Yongjun Liao, Pierre Geurts, Guy Leduc:
Ordinal Rating of Network Performance and Inference by Matrix Completion. CoRR abs/1211.0447 (2012)
Coauthor Index
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