Pierre Geurts

Pierre Geurts, Louis Wehenkel, Antia Blanco Cuesta, 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Decentralized prediction of end-to-end network performance classes

Pierre Geurts, Guy Leduc, Wei Du, 2011, CoNEXT '11.

Closed-form dual perturb and combine for tree-based models

Pierre Geurts, Louis Wehenkel, P. Geurts, 2005, ICML.

Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities

Alberto Del Angel, Pierre Geurts, Louis Wehenkel, 2007, Neurocomputing.

Improving the bias/variance tradeoff of decision trees: towards soft tree induction

Pierre Geurts, Louis Wehenkel, Cristina Olaru, 2001 .

Machine learning in systems biology: proceedings of The Third International Workshop, September 5-6, 2009, Ljubljana, Slovenia

Juho Rousu, Pierre Geurts, Saso Dzeroskig, 2009 .

Ensembles on Random Patches

Gilles Louppe, Pierre Geurts, Gilles Louppe, 2012, ECML/PKDD.

On protocols and measures for the validation of supervised methods for the inference of biological networks

Pierre Geurts, Robert Küffner, Marie Schrynemackers, 2013, Front. Genet..

Proceedings of the Third International Workshop on Machine Learning in Systems Biology: Revised Selected Papers

Juho Rousu, Pierre Geurts, Sašo Džeroski, 2009 .

Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification

Pierre Geurts, Louis Wehenkel, Arnaud Joly, 2014, ECML/PKDD.

Deriving p-values for tree-based variable importance measures

Pierre Geurts, Louis Wehenkel, Vân Anh Huynh-Thu, 2008 .

Deep Transfer Learning for Art Classification Problems

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2018, ECCV Workshops.

Comparator selection for RPC with many labels

Pierre Geurts, Louis Wehenkel, Samuel Hiard, 2012, ECAI.

Traitement de données volumineuses par ensemble d'arbres aléatoires

Pierre Geurts, 2003 .

Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms pdf

Gilles Louppe, Pierre Geurts, Matthia Sabatelli, 2019, NeurIPS 2019.

Globally Induced Forest: A Prepruning Compression Scheme

Pierre Geurts, Jean-Michel Begon, Arnaud Joly, 2017, ICML.

Kernelizing the output of tree-based methods

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc, 2006, ICML '06.

Supervised learning with decision tree-based methods in computational and systems biology.

Pierre Geurts, Louis Wehenkel, Alexandre Irrthum, 2009, Molecular bioSystems.

Network Distance Prediction Based on Decentralized Matrix Factorization

Pierre Geurts, Guy Leduc, Yongjun Liao, 2010, Networking.

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data

Pierre Geurts, Vân Anh Huynh-Thu, P. Geurts, 2018, Scientific Reports.

Collaborative analysis of multi-gigapixel imaging data using Cytomine

Gilles Louppe, Raphaël Marée, Pierre Geurts, 2016, Bioinform..

Gradient boosting for kernelized output spaces

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc, 2007, ICML '07.

Inferring biological networks with output kernel trees

Pierre Geurts, Florence d'Alché-Buc, Marie Dutreix, 2007, BMC Bioinformatics.

Bias vs Variance Decomposition for Regression and Classification

Pierre Geurts, P. Geurts, 2005, Data Mining and Knowledge Discovery Handbook.

Biological Image Classification with Random Subwindows and Extra-Trees

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2006 .

Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2013 .

A Semi-Algebraic Description of Naive Bayes Models with Two Hidden Classes

Pierre Geurts, Louis Wehenkel, Vincent Auvray, 2006, ISAIM.

Tree-Based Batch Mode Reinforcement Learning

Pierre Geurts, Louis Wehenkel, Damien Ernst, 2005, J. Mach. Learn. Res..

Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis

Marc Van Droogenbroeck, Pierre Geurts, Samir Azrour, 2014, ESANN.

Improving TCP in Wireless Networks with an Adaptive Machine-Learnt Classifier of Packet Loss Causes

Pierre Geurts, Guy Leduc, Ibtissam El Khayat, 2005, NETWORKING.

Completion of biological networks: the output kernel trees approach

Pierre Geurts, Marie Dutreix, Nizar Touleimat, 2006 .

Data mining tools and application in power system engineering

Pierre Geurts, Louis Wehenkel, Cristina Olaru, 1999 .

Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2006 .

Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments

Raphaël Marée, Pierre Geurts, Olivier Stern, 2010, CLEF.

Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods

Pierre Geurts, Louis Wehenkel, Francis Maes, 2012, ECML/PKDD.

On the Accuracy of Analytical Models of TCP Throughput

Pierre Geurts, Guy Leduc, Ibtissam El Khayat, 2006, Networking.

Some Enhencements of Decision Tree Bagging

Pierre Geurts, P. Geurts, 2000, PKDD.

Learning from positive and unlabeled examples by enforcing statistical significance

Pierre Geurts, P. Geurts, 2011, AISTATS.

Decision Trees and Random Subwindows for Object Recognition

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2005 .

Global multi-output decision trees for interaction prediction

Pierre Geurts, Konstantinos Pliakos, Celine Vens, 2018, Machine Learning.

Random Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2007 .

Pattern Extraction for Time Series Classification

Pierre Geurts, P. Geurts, 2001, PKDD.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery

Yvan Saeys, Pierre Geurts, Louis Wehenkel, 2012, Bioinform..

Temporal Machine Learning for Switching Control

Pierre Geurts, Louis Wehenkel, P. Geurts, 2000, PKDD.

Biomedical Image Classification with Random Subwindows and Decision Trees

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2005, CVBIA.

A machine learning approach for material detection in hyperspectral images

Raphaël Marée, Pierre Geurts, Benjamin Stevens, 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

SCENIC: Single-cell regulatory network inference and clustering

Pierre Geurts, Carmen Bravo González-Blas, Sara Aibar, 2017 .

Towards generic image classification: an extensive empirical study

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2014 .

Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning

Pierre Geurts, Guy Leduc, Ibtissam El Khayat, 2010, Wirel. Networks.

Dual perturb and combine algorithm

Pierre Geurts, P. Geurts, 2001, AISTATS.

Statistical interpretation of machine learning-based feature rankings for biomarker discovery

Yvan Saeys, Pierre Geurts, Louis Wehenkel, 2011 .

Fast Multi-class Image Annotation with Random Subwindows and Multiple Output Randomized Trees

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2009, VISAPP.

Extremely randomized trees

Pierre Geurts, Louis Wehenkel, Damien Ernst, 2006, Machine Learning.

A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes

Pierre Geurts, Pierre Faux, Tom Druet, 2019, Front. Genet..

Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease

Pierre Geurts, Christine Bastin, Christophe Phillips, 2017, 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

L1-based compression of random forest models

Pierre Geurts, Louis Wehenkel, François Schnitzler, 2012, ESANN.

Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data

Pierre Geurts, Minh Quach, 2006, ESANN.

Rating Network Paths for Locality-Aware Overlay Construction and Routing

Pierre Geurts, Xiaoming Fu, Guy Leduc, 2015, IEEE/ACM Transactions on Networking.

Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2011, PRIB.

Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection

Raphaël Marée, Pierre Geurts, Sébastien Jodogne, 2017, VISIGRAPP.

Random subwindows for robust image classification

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Comparison of Deep Transfer Learning Strategies for Digital Pathology

Raphaël Marée, Pierre Geurts, Romain Mormont, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction

Pierre Geurts, Guy Leduc, Wei Du, 2012, IEEE/ACM Transactions on Networking.

Cytomine: An Open-Source Software For Collaborative Analysis Of Whole-Slide Images

Gilles Louppe, Raphaël Marée, Pierre Geurts, 2016 .

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning pdf

Gilles Louppe, Pierre Geurts, Matthia Sabatelli, 2020, ArXiv.

Classifying pairs with trees for supervised biological network inference† †Electronic supplementary information (ESI) available: Implementation and computational issues, supplementary performance curves, and illustration of interpretability of trees. See DOI: 10.1039/c5mb00174a Click here for additi

Pierre Geurts, Louis Wehenkel, M. Madan Babu, 2014, Molecular bioSystems.

Automatic Landmark Detection in 2D images: A tree-based approach with multiresolution pixel features

Raphaël Marée, Pierre Geurts, Sébastien Jodogne, 2014 .

Bias vs Variance Decomposition for Regression and Classification

Pierre Geurts, 2005 .

Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2021, Electronic Imaging.

On the Transferability of Winning Tickets in Non-Natural Image Datasets

Pierre Geurts, Mike Kestemont, Matthia Sabatelli, 2020, VISIGRAPP.

From global to local MDI variable importances for random forests and when they are Shapley values pdf

Gilles Louppe, Pierre Geurts, Louis Wehenkel, 2021, NeurIPS.

Sample-free white-box out-of-distribution detection for deep learning

Pierre Geurts, Jean-Michel Begon, P. Geurts, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Evaluation of Local Model-Agnostic Explanations Using Ground Truth pdf

Pierre Geurts, Henrik Bostrom, Amir Hossein Akhavan Rahnama, 2021, ArXiv.

On The Transferability of Deep-Q Networks pdf

Pierre Geurts, Matthia Sabatelli, P. Geurts, 2021, ArXiv.

Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images

Raphaël Marée, Pierre Geurts, Navdeep Kumar, 2021, CAIP.

Distillation from heterogeneous unlabeled collections pdf

Pierre Geurts, Jean-Michel Begon, 2022, ArXiv.

Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2021, Digit. Humanit. Q..

Optimizing model-agnostic Random Subspace ensembles pdf

Pierre Geurts, Van Anh Huynh-Thu, 2021, ArXiv.

Machine-learnt versus analytical models of TCP throughput

Pierre Geurts, Guy Leduc, Ibtissam El Khayat, 2007, Comput. Networks.

A screening methodology based on Random Forests to improve the detection of gene–gene interactions

Pierre Geurts, Guy Baele, Kristel Van Steen, 2010, European Journal of Human Genetics.

Bridging physiological and evolutionary time-scales in a gene regulatory network.

Pierre Geurts, Matthieu Vignes, Vân Anh Huynh-Thu, 2013, The New phytologist.

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2012, PloS one.

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2012, PloS one.

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference.

Pierre Geurts, Stein Aerts, Gert Hulselmans, 2014, Cell reports.

Automated processing of zebrafish imaging data: a survey.

Raphaël Marée, Pierre Geurts, Ralf Mikut, 2013, Zebrafish.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

Piet Demeester, Yvan Saeys, Tom Dhaene, 2014, PloS one.

Automatic Cephalometric X-Ray Landmark Detection Challenge 2014: A machine learning tree-based approach

Raphaël Marée, Pierre Geurts, Sébastien Jodogne, 2014 .

An empirical comparison of machine learning algorithms for generic image classification

Raphaël Marée, Pierre Geurts, Louis Wehenkel, 2003 .