Investigation and Reduction of Discretization Variance in Decision Tree Induction. - Pierre Geurts; Louis Wehenkel (ECML, 2000)

Some Enhencements of Decision Tree Bagging. - Pierre Geurts (PKDD, 2000)

Temporal Machine Learning for Switching Control. - Pierre Geurts; Louis Wehenkel (PKDD, 2000)

Dual perturb and combine algorithm. - Pierre Geurts (AISTATS, 2001)

Pattern Extraction for Time Series Classification. - Pierre Geurts (PKDD, 2001)

Contributions to decision tree induction: bias/variance tradeoff and time series classification. - Pierre Geurts (2002)

Iteratively Extending Time Horizon Reinforcement Learning. - Damien Ernst; Pierre Geurts; Louis Wehenkel (ECML, 2003)

A Comparison of Generic Machine Learning Algorithms for Image Classification. - Raphaël Marée; Pierre Geurts; Giorgio Visimberga; Justus H. Piater; Louis Wehenkel (SGAI Conf., 2003)

A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks. - Pierre Geurts; Ibtissam El Khayat; Guy Leduc (ICDM, 2004)

Bias vs. Variance Decomposition for Regression and Classification. - Pierre Geurts (The Data Mining and Knowledge Discovery Handbook, 2005)

Segment and Combine Approach for Biological Sequence Classification. - Pierre Geurts; Antia Blanco Cuesta; Louis Wehenkel (CIBCB, 2005)

Biomedical Image Classification with Random Subwindows and Decision Trees. - Raphaël Marée; Pierre Geurts; Justus H. Piater; Louis Wehenkel (CVBIA, 2005)

Random Subwindows for Robust Image Classification. - Raphaël Marée; Pierre Geurts; Justus H. Piater; Louis Wehenkel (CVPR (1), 2005)

Closed-form dual perturb and combine for tree-based models. - Pierre Geurts; Louis Wehenkel (ICML, 2005)

Improving TCP in Wireless Networks with an Adaptive Machine-Learnt Classifier of Packet Loss Causes. - Ibtissam El Khayat; Pierre Geurts; Guy Leduc (NETWORKING, 2005)

Segment and Combine Approach for Non-parametric Time-Series Classification. - Pierre Geurts; Louis Wehenkel (PKDD, 2005)

Proteomic mass spectra classification using decision tree based ensemble methods. - Pierre Geurts; Marianne Fillet; Dominique de Seny; Marie-Alice Meuwis; Michel Malaise; Marie-Paule Merville; Louis Wehenkel (Bioinform., 2005)

Tree-Based Batch Mode Reinforcement Learning. - Damien Ernst; Pierre Geurts; Louis Wehenkel (J. Mach. Learn. Res., 2005)

Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data. - Minh Quach; Pierre Geurts (ESANN, 2006)

Kernelizing the output of tree-based methods. - Pierre Geurts; Louis Wehenkel; Florence d'Alché-Buc (ICML, 2006)

A Semi-Algebraic Description of Naive Bayes Models with Two Hidden Classes. - Vincent Auvray; Pierre Geurts; Louis Wehenkel (AI&M, 2006)

On the Accuracy of Analytical Models of TCP Throughput. - Ibtissam El Khayat; Pierre Geurts; Guy Leduc (Networking, 2006)

Extremely randomized trees. - Pierre Geurts; Damien Ernst; Louis Wehenkel (Mach. Learn., 2006)

Gradient boosting for kernelized output spaces. - Pierre Geurts; Louis Wehenkel; Florence d'Alché-Buc (ICML, 2007)

Inferring biological networks with output kernel trees. - Pierre Geurts; Nizar Touleimat; Marie Dutreix; Florence d'Alché-Buc (BMC Bioinform., 2007)

Machine-learnt versus analytical models of TCP throughput. - Ibtissam El Khayat; Pierre Geurts; Guy Leduc (Comput. Networks, 2007)

Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. - Alberto Del Angel; Pierre Geurts; Damien Ernst; Mevludin Glavic; Louis Wehenkel (Neurocomputing, 2007)

Exploiting tree-based variable importances to selectively identify relevant variables. - Vân Anh Huynh-Thu; Louis Wehenkel; Pierre Geurts (FSDM, 2008)

A machine learning approach for material detection in hyperspectral images. - Raphaël Marée; Benjamin Stevens; Pierre Geurts; Y. Guern; P. Mack (CVPR Workshops, 2009)

Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning. - Yongjun Liao; Mohamed Ali Kâafar; Bamba Gueye; François Cantin; Pierre Geurts; Guy Leduc (Networking, 2009)

Fast Multi-class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. - Marie Dumont; Raphaël Marée; Louis Wehenkel; Pierre Geurts (VISAPP (2), 2009)

Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. - Raphaël Marée; Pierre Geurts; Louis Wehenkel (IPSJ Trans. Comput. Vis. Appl., 2009)

Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments. - Raphaël Marée; Olivier Stern; Pierre Geurts (CLEF (Notebook Papers/LABs/Workshops), 2010)

Incremental indexing and distributed image search using shared randomized vocabularies. - Raphaël Marée; Philippe Denis; Louis Wehenkel; Pierre Geurts (Multimedia Information Retrieval, 2010)

Network Distance Prediction Based on Decentralized Matrix Factorization. - Yongjun Liao; Pierre Geurts; Guy Leduc (Networking, 2010)

Preface. - Saso Dzeroski; Pierre Geurts; Juho Rousu (MLSB, 2010)

Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. - Ibtissam El Khayat; Pierre Geurts; Guy Leduc (Wirel. Networks, 2010)

Bias vs Variance Decomposition for Regression and Classification. - Pierre Geurts (Data Mining and Knowledge Discovery Handbook, 2010)

Decentralized prediction of end-to-end network performance classes. - Yongjun Liao; Wei Du; Pierre Geurts; Guy Leduc (CoNEXT, 2011)

Efficiently Approximating Markov Tree Bagging for High-Dimensional Density Estimation. - François Schnitzler; Sourour Ammar; Philippe Leray 001; Pierre Geurts; Louis Wehenkel (ECML/PKDD (3), 2011)

Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods. - Olivier Stern; Raphaël Marée; Jessica Aceto; Nathalie Jeanray; Marc Muller; Louis Wehenkel; Pierre Geurts (PRIB, 2011)

Learning from positive and unlabeled examples by enforcing statistical significance. - Pierre Geurts (AISTATS, 2011)

Learning to rank with extremely randomized trees. - Pierre Geurts; Gilles Louppe (Yahoo! Learning to Rank Challenge, 2011)

Comparator selection for RPC with many labels. - Samuel Hiard; Pierre Geurts; Louis Wehenkel (ECAI, 2012)

L1-based compression of random forest models. - Arnaud Joly; François Schnitzler; Pierre Geurts; Louis Wehenkel (ESANN, 2012)

Ensembles on Random Patches. - Gilles Louppe; Pierre Geurts (ECML/PKDD (1), 2012)

Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods. - Francis Maes; Pierre Geurts; Louis Wehenkel (ECML/PKDD (1), 2012)

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. - Vân Anh Huynh-Thu; Yvan Saeys; Louis Wehenkel; Pierre Geurts (Bioinform., 2012)

Ordinal Rating of Network Performance and Inference by Matrix Completion - Wei Du; Yongjun Liao; Pierre Geurts; Guy Leduc (CoRR, 2012)

Understanding variable importances in forests of randomized trees. - Gilles Louppe; Louis Wehenkel; Antonio Sutera; Pierre Geurts (NIPS, 2013)

DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. - Yongjun Liao; Wei Du; Pierre Geurts; Guy Leduc (IEEE/ACM Trans. Netw., 2013)

Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging. - Antonio Sutera; Arnaud Joly; Vincent François-Lavet; Zixiao Aaron Qiu; Gilles Louppe; Damien Ernst; Pierre Geurts (Neural Connectomics, 2014)

Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. - Samir Azrour; Sébastien Piérard; Pierre Geurts; Marc Van Droogenbroeck (ESANN, 2014)

Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification. - Arnaud Joly; Pierre Geurts; Louis Wehenkel (ECML/PKDD (1), 2014)

Classifying pairs with trees for supervised biological network inference. - Marie Schrynemackers; Louis Wehenkel; M. Madan Babu; Pierre Geurts (CoRR, 2014)

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. - 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 022; Guoyan Zheng; et al. (IEEE Trans. Medical Imaging, 2015)

Rating Network Paths for Locality-Aware Overlay Construction and Routing. - Wei Du; Yongjun Liao; Narisu Tao; Pierre Geurts; Xiaoming Fu 001; Guy Leduc (IEEE/ACM Trans. Netw., 2015)

Context-dependent feature analysis with random forests. - Antonio Sutera; Gilles Louppe; Vân Anh Huynh-Thu; Louis Wehenkel; Pierre Geurts (UAI, 2016)

Collaborative analysis of multi-gigapixel imaging data using Cytomine. - Raphaël Marée; Loic Rollus; Benjamin Stevens; Renaud Hoyoux; Gilles Louppe; Remy Vandaele; Jean-Michel Begon; Philipp Kainz; Pierre Geurts; Louis Wehenkel (Bioinform., 2016)

Towards generic image classification using tree-based learning: An extensive empirical study. - Raphaël Marée; Pierre Geurts; Louis Wehenkel (Pattern Recognit. Lett., 2016)

A Two-Step Methodology for Human Pose Estimation Increasing the Accuracy and Reducing the Amount of Learning Samples Dramatically. - Samir Azrour; Sébastien Piérard; Pierre Geurts; Marc Van Droogenbroeck (ACIVS, 2017)

Globally Induced Forest: A Prepruning Compression Scheme. - Jean-Michel Begon; Arnaud Joly; Pierre Geurts (ICML, 2017)

Tree ensemble methods and parcelling to identify brain areas related to Alzheimer's disease. - M. Wehenkel; Christine Bastin; Christophe Phillips; Pierre Geurts (PRNI, 2017)

Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection. - Remy Vandaele; François Lallemand; Philippe Martinive; Akos Gulyban; Sébastien Jodogne; Philippe Coucke; Pierre Geurts; Raphaël Marée (VISIGRAPP (5: VISAPP), 2017)

Random Subspace with Trees for Feature Selection Under Memory Constraints. - Antonio Sutera; Célia Châtel; Gilles Louppe; Louis Wehenkel; Pierre Geurts (AISTATS, 2018)

Comparison of Deep Transfer Learning Strategies for Digital Pathology. - Romain Mormont; Pierre Geurts; Raphaël Marée (CVPR Workshops, 2018)

Deep Transfer Learning for Art Classification Problems. - Matthia Sabatelli; Mike Kestemont; Walter Daelemans; Pierre Geurts (ECCV Workshops (2), 2018)

Global multi-output decision trees for interaction prediction. - Konstantinos Pliakos; Pierre Geurts; Celine Vens (Mach. Learn., 2018)

Deep Quality-Value (DQV) Learning. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (BNAIC/BENELEARN, 2019)

Gradient tree boosting with random output projections for multi-label classification and multi-output regression. - Arnaud Joly; Louis Wehenkel; Pierre Geurts (CoRR, 2019)

Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (CoRR, 2019)

Nets Versus Trees for Feature Ranking and Gene Network Inference. - Nicolas Vecoven; Jean-Michel Begon; Antonio Sutera; Pierre Geurts; Vân Anh Huynh-Thu (DS, 2020)

The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (IJCNN, 2020)

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. - Pascal Leroy; Damien Ernst; Pierre Geurts; Gilles Louppe; Jonathan Pisane; Matthia Sabatelli (CoRR, 2020)

Error curves for evaluating the quality of feature rankings. - Ivica Slavkov; Matej Petkovic; Pierre Geurts; Dragi Kocev; Saso Dzeroski (PeerJ Comput. Sci., 2020)

Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images. - Navdeep Kumar; Alessio Carletti; Paulo Jorge Gavaia; Marc Muller; M. Leonor Cancela; Pierre Geurts; Raphaël Marée (CAIP (1), 2021)

Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. - Jean-Michel Begon; Pierre Geurts (CVPR Workshops, 2021)

Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain. - Nikolay Banar; Matthia Sabatelli; Pierre Geurts; Walter Daelemans; Mike Kestemont (Computer Vision and Image Analysis of Art, 2021)

From global to local MDI variable importances for random forests and when they are Shapley values. - Antonio Sutera; Gilles Louppe; Vân Anh Huynh-Thu; Louis Wehenkel; Pierre Geurts (NeurIPS, 2021)

On the Transferability of Winning Tickets in Non-natural Image Datasets. - Matthia Sabatelli; Mike Kestemont; Pierre Geurts (VISIGRAPP (5: VISAPP), 2021)

Evaluation of Local Model-Agnostic Explanations Using Ground Truth. - Amir Hossein Akhavan Rahnama; Judith Bütepage; Pierre Geurts; Henrik Boström (CoRR, 2021)

On The Transferability of Deep-Q Networks. - Matthia Sabatelli; Pierre Geurts (CoRR, 2021)

Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. - Matthia Sabatelli; Nikolay Banar; Marie Cocriamont; Eva Coudyzer; Karine Lasaracina; Walter Daelemans; Pierre Geurts; Mike Kestemont (Digit. Humanit. Q., 2021)

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology. - Romain Mormont; Pierre Geurts; Raphaël Marée (IEEE J. Biomed. Health Informatics, 2021)

Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages. - Navdeep Kumar; Claudia Di Biagio; Zachary Dellacqua; Ratish Raman; Arianna Martini; Clara Boglione; Marc Muller; Pierre Geurts; Raphaël Marée (ECCV Workshops (4), 2022)

Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-training. - Romain Mormont; Mehdi Testouri; Raphaël Marée; Pierre Geurts (ECCV Workshops (7), 2022)

Distillation from heterogeneous unlabeled collections. - Jean-Michel Begon; Pierre Geurts (CoRR, 2022)

Knowledge-Guided Additive Modeling for Supervised Regression. - Yann Claes; Vân Anh Huynh-Thu; Pierre Geurts (DS, 2023)

Faithfulness of Local Explanations for Tree-Based Ensemble Models. - Amir Hossein Akhavan Rahnama; Pierre Geurts; Henrik Boström (DS (2), 2024)

Trust in Artificial Intelligence: Beyond Interpretability. - Tassadit Bouadi; Benoît Frénay; Luis Galárraga; Pierre Geurts; Barbara Hammer; Gilles Perrouin (ESANN, 2024)

Parallelizing Autoregressive Generation with Variational State Space Models. - Gaspard Lambrechts; Yann Claes; Pierre Geurts; Damien Ernst (CoRR, 2024)

Can local explanation techniques explain linear additive models? - Amir Hossein Akhavan Rahnama; Judith Bütepage; Pierre Geurts; Henrik Boström (Data Min. Knowl. Discov., 2024)

Optimizing model-agnostic random subspace ensembles. - Vân Anh Huynh-Thu; Pierre Geurts (Mach. Learn., 2024)

Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting. - Yann Claes; Vân Anh Huynh-Thu; Pierre Geurts (Mach. Learn., 2025)