QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games. - Benoît Frénay; Marco Saerens (ESANN, 2008)

Improving the transition modelling in hidden Markov models for ECG segmentation. - Benoît Frénay; Gaël de Lannoy; Michel Verleysen (ESANN, 2009)

2S2, a simple reinforcement learning scheme for two-player zero-sum Markov games. - Benoît Frénay; Marco Saerens (Neurocomputing, 2009)

Using SVMs with randomised feature spaces: an extreme learning approach. - Benoît Frénay; Michel Verleysen (ESANN, 2010)

Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs. - Benoît Frénay; Gaël de Lannoy; Michel Verleysen (ECML/PKDD (1), 2011)

Parameter-insensitive kernel in extreme learning for non-linear support vector regression. - Benoît Frénay; Michel Verleysen (Neurocomputing, 2011)

On the Potential Inadequacy of Mutual Information for Feature Selection. - Benoît Frénay; Gauthier Doquire; Michel Verleysen (ESANN, 2012)

Risk Estimation and Feature Selection. - Gauthier Doquire; Benoît Frénay; Michel Verleysen (ESANN, 2013)

Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification. - Benoît Frénay; Gauthier Doquire; Michel Verleysen (Neurocomputing, 2013)

Feature selection for nonlinear models with extreme learning machines. - Benoît Frénay; Mark van Heeswijk; Yoan Miche; Michel Verleysen; Amaury Lendasse (Neurocomputing, 2013)

Is mutual information adequate for feature selection in regression? - Benoît Frénay; Gauthier Doquire; Michel Verleysen (Neural Networks, 2013)

Uncertainty and label noise in machine learning. - Benoît Frénay (2013)

Valid interpretation of feature relevance for linear data mappings. - Benoît Frénay; Daniela Hofmann; Alexander Schulz 001; Michael Biehl; Barbara Hammer (CIDM, 2014)

A comprehensive introduction to label noise. - Benoît Frénay; Ata Kabán (ESANN, 2014)

Automatic correction of SVM for drifted data classification. - Alexandra Degeest; Benoît Frénay; Michel Verleysen (EGC, 2014)

Estimating mutual information for feature selection in the presence of label noise. - Benoît Frénay; Gauthier Doquire; Michel Verleysen (Comput. Stat. Data Anal., 2014)

Pointwise probability reinforcements for robust statistical inference. - Benoît Frénay; Michel Verleysen (Neural Networks, 2014)

Classification in the Presence of Label Noise: A Survey. - Benoît Frénay; Michel Verleysen (IEEE Trans. Neural Networks Learn. Syst., 2014)

Survival Analysis with Cox Regression and Random Non-linear Projections. - Samuel Branders; Benoît Frénay; Pierre Dupont (ESANN, 2015)

Feature ranking in changing environments where new features are introduced. - Alexandra Degeest; Michel Verleysen; Benoît Frénay (IJCNN, 2015)

Special issue on advances in learning with label noise. - Benoît Frénay; Ata Kabán (Neurocomputing, 2015)

Interpretability of machine learning models and representations: an introduction. - Adrien Bibal; Benoît Frénay (ESANN, 2016)

Information visualisation and machine learning: characteristics, convergence and perspective. - Benoît Frénay; Bruno Dumas (ESANN, 2016)

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. - Adrien Bibal; Benoît Frénay (CoRR, 2016)

Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers. - Benoît Frénay; Michel Verleysen (IEEE Trans. Cybern., 2016)

Label-noise-tolerant classification for streaming data. - Benoît Frénay; Barbara Hammer (IJCNN, 2017)

Finding the most interpretable MDS rotation for sparse linear models based on external features. - Adrien Bibal; Rebecca Marion; Benoît Frénay (ESANN, 2018)

clustering with decision trees: divisive and agglomerative approach. - Lauriane Castin; Benoît Frénay (ESANN, 2018)

Information visualisation and machine learning: latest trends towards convergence. - Benoît Frénay; Bruno Dumas; John A. Lee 001 (ESANN, 2018)

Smoothness Bias in Relevance Estimators for Feature Selection in Regression. - Alexandra Degeest; Michel Verleysen; Benoît Frénay (AIAI, 2018)

Deep Learning Applied to Sign Language. - Jérôme Fink; Anthony Cleve; Benoît Frénay (BNAIC/BENELEARN, 2019)

User-steering interpretable visualization with probabilistic principal components analysis. - Viet Minh Vu; Benoît Frénay (ESANN, 2019)

Comparison Between Filter Criteria for Feature Selection in Regression. - Alexandra Degeest; Michel Verleysen; Benoît Frénay (ICANN (2), 2019)

About Filter Criteria for Feature Selection in Regression. - Alexandra Degeest; Michel Verleysen; Benoît Frénay (IWANN (2), 2019)

BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables. - Rebecca Marion; Adrien Bibal; Benoît Frénay (Neurocomputing, 2019)

AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. - Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas (CIKM, 2020)

Explaining t-SNE Embeddings Locally by Adapting LIME. - Adrien Bibal; Viet Minh Vu; Géraldin Nanfack; Benoît Frénay (ESANN, 2020)

Impact of Legal Requirements on Explainability in Machine Learning. - Adrien Bibal; Michael Lognoul; Alexandre de Streel; Benoît Frénay (CoRR, 2020)

Function completion in the time of massive data: A code embedding perspective. - Martin Weyssow; Houari A. Sahraoui; Benoît Vanderose; Benoît Frénay (CoRR, 2020)

Report from the 1st Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '19). - Benoît Vanderose; Benoît Frénay; Julie Henry; Xavier Devroey (ACM SIGSOFT Softw. Eng. Notes, 2020)

Boundary-Based Fairness Constraints in Decision Trees and Random Forests. - Géraldin Nanfack; Valentin Delchevalerie; Benoît Frénay (ESANN, 2021)

Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics. - Valentin Delchevalerie; Alexandre Mayer; Adrien Bibal; Benoît Frénay (IJCNN, 2021)

LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition. - Jérôme Fink; Benoît Frénay; Laurence Meurant; Anthony Cleve (IJCNN, 2021)

iPMDS: Interactive Probabilistic Multidimensional Scaling. - Viet Minh Vu; Adrien Bibal; Benoît Frénay (IJCNN, 2021)

HCt-SNE: Hierarchical Constraints with t-SNE. - Viet Minh Vu; Adrien Bibal; Benoît Frénay (IJCNN, 2021)

GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case. - Arnaud Bougaham; Adrien Bibal; Isabelle Linden; Benoît Frénay (LIDTA@ECML/PKDD, 2021)

Achieving Rotational Invariance with Bessel-Convolutional Neural Networks. - Valentin Delchevalerie; Adrien Bibal; Benoît Frénay; Alexandre Mayer (NeurIPS, 2021)

Open Government Data for Non-expert Citizens: Understanding Content and Visualizations' Expectations. - Abiola Paterne Chokki; Anthony Simonofski; Benoît Frénay; Benoît Vanderose (RCIS, 2021)

A tool for evaluating computer programs from students. - Quentin Vaneck; Thomas Colart; Benoît Frénay; Benoît Vanderose (EASEAI@ESEC/SIGSOFT FSE, 2021)

Global explanations with decision rules: a co-learning approach. - Géraldin Nanfack; Paul Temple; Benoît Frénay (UAI, 2021)

Legal requirements on explainability in machine learning. - Adrien Bibal; Michael Lognoul; Alexandre de Streel; Benoît Frénay (Artif. Intell. Law, 2021)

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees. - Adrien Bibal; Antoine Clarinval; Bruno Dumas; Benoît Frénay (Array, 2021)

DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality. - Cristina Morariu; Adrien Bibal; René Cutura; Benoît Frénay; Michael Sedlmair (CoRR, 2021)

BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation. - Adrien Bibal; Rebecca Marion; Rainer von Sachs; Benoît Frénay (Neurocomputing, 2021)

Reading grid for feature selection relevance criteria in regression. - Alexandra Degeest; Benoît Frénay; Michel Verleysen (Pattern Recognit. Lett., 2021)

Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. - Pieter Delobelle; Paul Temple; Gilles Perrouin; Benoît Frénay; Patrick Heymans; Bettina Berendt (SIGKDD Explor., 2021)

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20). - Benoît Vanderose; Julie Henry; Benoît Frénay; Xavier Devroey (ACM SIGSOFT Softw. Eng. Notes, 2021)

Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization. - Viet Minh Vu; Adrien Bibal; Benoît Frénay (IEEE Trans. Artif. Intell., 2021)

Open Data Explorer: An End-to-end Tool for Data Storytelling using Open Data. - Abiola Paterne Chokki; Benoît Frénay; Benoît Vanderose (AMCIS, 2022)

AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence. - Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas (CIKM, 2022)

ODSAG: Enhancing Open Data Discoverability and Understanding through Semantic Annotation (short paper). - Abiola Paterne Chokki; Rabeb Abida; Benoît Frénay; Benoît Vanderose; Anthony Cleve (EGOV-CeDEM-ePart-*, 2022)

Fostering Interaction Between Open Government Data Stakeholders: An Exchange Platform for Citizens, Developers and Publishers. - Abiola Paterne Chokki; Anthony Simonofski; Antoine Clarinval; Benoît Frénay; Benoît Vanderose (EGOV, 2022)

Increasing Awareness and Usefulness of Open Government Data: An Empirical Analysis of Communication Methods. - Abiola Paterne Chokki; Anthony Simonofski; Benoît Frénay; Benoît Vanderose (RCIS, 2022)

Engaging Citizens with Open Government Data: The Value of Dashboards Compared to Individual Visualizations. - Abiola Paterne Chokki; Anthony Simonofski; Benoît Frénay; Benoît Vanderose (Digit. Gov. Res. Pract., 2022)

Learning from Code Repositories to Recommend Model Classes. - Thibaut Capuano; Houari A. Sahraoui; Benoît Frénay; Benoît Vanderose (J. Object Technol., 2022)

Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey. - Viet Minh Vu; Adrien Bibal; Benoît Frénay (IEEE Trans. Artif. Intell., 2022)

Constraint Enforcement on Decision Trees: A Survey. - Géraldin Nanfack; Paul Temple; Benoît Frénay (ACM Comput. Surv., 2022)

Trends and Challenges for Sign Language Recognition with Machine Learning. - Jérôme Fink; Mathieu De Coster; Joni Dambre; Benoît Frénay (ESANN, 2023)

A Counterexample to Ockham's Razor and the Curse of Dimensionality: Marginalising Complexity and Dimensionality for GMMs. - Benoît Frénay (ESANN, 2023)

FairBayRank: A Fair Personalized Bayesian Ranker. - Armielle Noulapeu Ngaffo; Julien Albert; Benoît Frénay; Gilles Perrouin (ESANN, 2023)

Sign Language-to-Text Dictionary with Lightweight Transformer Models. - Jérôme Fink; Pierre Poitier; Maxime André 001; Loup Meurice; Benoît Frénay; Anthony Cleve; Bruno Dumas; Laurence Meurant (IJCAI, 2023)

An Experimental Investigation into the Evaluation of Explainability Methods for Computer Vision. - Sédrick Stassin; Alexandre Englebert; Géraldin Nanfack; Julien Albert; Nassim Versbraegen; Gilles Peiffer; Miriam Doh; Nicolas Riche; Benoît Frénay; Christophe De Vleeschouwer (PKDD/ECML Workshops (1), 2023)

SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks. - Valentin Delchevalerie; Alexandre Mayer; Adrien Bibal; Benoît Frénay (CoRR, 2023)

An Experimental Investigation into the Evaluation of Explainability Methods. - Sédrick Stassin; Alexandre Englebert; Géraldin Nanfack; Julien Albert; Nassim Versbraegen; Gilles Peiffer; Miriam Doh; Nicolas Riche; Benoît Frénay; Christophe De Vleeschouwer (CoRR, 2023)

Predicting User Preferences of Dimensionality Reduction Embedding Quality. - Cristina Morariu; Adrien Bibal; René Cutura; Benoît Frénay; Michael Sedlmair (IEEE Trans. Vis. Comput. Graph., 2023)

Decision trees: from efficient prediction to responsible AI. - Hendrik Blockeel; Laurens Devos; Benoît Frénay; Géraldin Nanfack; Siegfried Nijssen (Frontiers Artif. Intell., 2023)

DT-SNE: t-SNE discrete visualizations as decision tree structures. - Adrien Bibal; Valentin Delchevalerie; Benoît Frénay (Neurocomputing, 2023)

Learning Customised Decision Trees for Domain-knowledge Constraints. - Géraldin Nanfack; Paul Temple; Benoît Frénay (Pattern Recognit., 2023)

Deep Learning for in vivo Bronchial Carinae Detection in Flexible Bronchoscopy. - Robin Ghyselinck; Valentin Delchevalerie; Pierre Poitier; Benoît Frénay; Bruno Dumas (ECAI, 2024)

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

ChatDT: Simplifying Constraint Integration in Decision Trees. - Abiola Paterne Chokki; Benoît Frénay (ESANN, 2024)

Insight-SNE: Understanding t-SNE Embeddings through Interactive Explanation. - Sacha Corbugy; Thibaut Septon; Bruno Dumas; Benoît Frénay (ESANN, 2024)

From Three to Two Dimensions: 2D Quaternion Convolutions for 3D Images. - Valentin Delchevalerie; Benoît Frénay; Alexandre Mayer (ESANN, 2024)

Leveraging endoscopic data with Contrastive Learning for Crohn's disease detection. - Robin Ghyselinck; Jérôme Fink; Bruno Dumas; Benoît Frénay (ESANN, 2024)

Extrapolating Venusian Atmospheric Profiles using MAGMA Gaussian Processes. - Simon Lejoly; Arianna Piccialli; Arnaud Mahieux; Ann-Carine Vandaele; Benoît Frénay (ESANN, 2024)

Fast k-means with Stable Instance Sets. - Ariel Basso Madjoukeng; Kenmogne Edith Belise; Benoît Frénay (IJCNN, 2024)

Towards better transition modeling in recurrent neural networks: The case of sign language tokenization. - Pierre Poitier; Jérôme Fink; Benoît Frénay (Neurocomputing, 2024)

Improving the Feature Selection Stability of the Delta Test in Regression. - Rebecca Marion; Benoît Frénay (IEEE Trans. Artif. Intell., 2024)

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors. - Mohammed El Adoui; Thomas Herpoel; Benoît Frénay (ACM Trans. Embed. Comput. Syst., 2024)

Composite score for anomaly detection in imbalanced real-world industrial dataset. - Arnaud Bougaham; Mohammed El Adoui; Isabelle Linden; Benoît Frénay (Mach. Learn., 2024)

Gradient-based explanation for non-linear non-parametric dimensionality reduction. - Sacha Corbugy; Rebecca Marion; Benoît Frénay (Data Min. Knowl. Discov., 2024)

Do open government data (OGD) portals show signs of knowledge management (KM) practices?: an empirical investigation. - Abiola Paterne Chokki; Charalampos Alexopoulos; Ricardo Matheus; Stuti Saxena; Benoît Frénay; Benoît Vanderose (Technol. Anal. Strateg. Manag., 2024)

Local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition. - Ariel Basso Madjoukeng; Kenmogne Edith Belise; Pierre Poitier; Benoît Frénay; Jérôme Fink (IDA, 2025)

Industrial and medical anomaly detection through cycle-consistent adversarial networks. - Arnaud Bougaham; Valentin Delchevalerie; Mohammed El Adoui; Benoît Frénay (Neurocomputing, 2025)

On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation. - Robin Ghyselinck; Valentin Delchevalerie; Bruno Dumas; Benoît Frénay (Trans. Mach. Learn. Res., 2025)

Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC Loss. - Arnaud Bougaham; Benoît Frénay (CoRR, 2025)