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

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

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

ML + FV = ♡? A Survey on the Application of Machine Learning to Formal Verification. - Moussa Amrani; Levi Lúcio; Adrien Bibal (CoRR, 2018)

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)

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction - Adrien Bibal (2020)

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

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)

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)

Is Attention Explanation? An Introduction to the Debate. - Adrien Bibal; Rémi Cardon; David Alfter; Rodrigo Wilkens; Xiaoou Wang; Thomas François; Patrick Watrin (ACL (1), 2022)

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

Linguistic Corpus Annotation for Automatic Text Simplification Evaluation. - Rémi Cardon; Adrien Bibal; Rodrigo Wilkens; David Alfter; Magali Norré; Adeline Müller; Patrick Watrin; Thomas François (EMNLP, 2022)

L'Attention est-elle de l'Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate ). - Adrien Bibal; Rémi Cardon; David Alfter; Rodrigo Wilkens; Xiaoou Wang; Thomas François; Patrick Watrin (TALN-RECITAL, 2022)

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

Annotation Linguistique pour l'Évaluation de la Simplification Automatique de Textes. - Rémi Cardon; Adrien Bibal; Rodrigo Wilkens; David Alfter; Magali Norré; Adeline Müller; Patrick Watrin; Thomas François (CORIA-TALN (4), 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)

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

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

Granting GPT-4 License and Opportunity: Enhancing Accuracy and Confidence Estimation for Few-Shot Event Detection. - Steven Fincke; Adrien Bibal; Elizabeth Boschee (CoRR, 2024)

RecSOI: recommending research directions using statements of ignorance. - Adrien Bibal; Nourah M. Salem; Rémi Cardon; Elizabeth K. White; Daniel E. Acuna; Robin Burke; Lawrence E. Hunter (J. Biomed. Semant., 2024)

Automating Annotation Guideline Improvements using LLMs: A Case Study. - Adrien Bibal; Nathaniel Gerlek; Goran Muric; Elizabeth Boschee; Steven Fincke; Mike Ross; Steven N. Minton (COLING Workshops, 2025)

AI Copilots for Reproducibility in Science: A Case Study. - Adrien Bibal; Steven N. Minton; Deborah Khider; Yolanda Gil (CoRR, 2025)