Random-shapelet: An algorithm for fast shapelet discovery. - Xavier Renard; Maria Rifqi; Walid Erray; Marcin Detyniecki (DSAA, 2015)

Inverse Classification for Comparison-based Interpretability in Machine Learning. - Thibault Laugel; Marie-Jeanne Lesot; Christophe Marsala; Xavier Renard; Marcin Detyniecki (CoRR, 2017)

Time series representation for classification: a motif-based approach. (Représentation de séries temporelles pour la classification: une approche basée sur la découverte automatique de motifs). - Xavier Renard (2017)

Comparison-Based Inverse Classification for Interpretability in Machine Learning. - Thibault Laugel; Marie-Jeanne Lesot; Christophe Marsala; Xavier Renard; Marcin Detyniecki (IPMU (1), 2018)

Detecting Potential Local Adversarial Examples for Human-Interpretable Defense. - Xavier Renard; Thibault Laugel; Marie-Jeanne Lesot; Christophe Marsala; Marcin Detyniecki (Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, 2018)

Defining Locality for Surrogates in Post-hoc Interpretablity. - Thibault Laugel; Xavier Renard; Marie-Jeanne Lesot; Christophe Marsala; Marcin Detyniecki (CoRR, 2018)

The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations. - Thibault Laugel; Marie-Jeanne Lesot; Christophe Marsala; Xavier Renard; Marcin Detyniecki (IJCAI, 2019)

Localized Random Shapelets. - Maël Guillemé; Simon Malinowski; Romain Tavenard; Xavier Renard (AALTD@PKDD/ECML, 2019)

Unjustified Classification Regions and Counterfactual Explanations in Machine Learning. - Thibault Laugel; Marie-Jeanne Lesot; Christophe Marsala; Xavier Renard; Marcin Detyniecki (ECML/PKDD (2), 2019)

Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees. - Xavier Renard; Nicolas Woloszko; Jonathan Aigrain; Marcin Detyniecki (CoRR, 2019)

Imperceptible Adversarial Attacks on Tabular Data. - Vincent Ballet; Xavier Renard; Jonathan Aigrain; Thibault Laugel; Pascal Frossard; Marcin Detyniecki (CoRR, 2019)

Sentence-Based Model Agnostic NLP Interpretability. - Yves Rychener; Xavier Renard; Djamé Seddah; Pascal Frossard; Marcin Detyniecki (CoRR, 2020)

QUACKIE: A NLP Classification Task With Ground Truth Explanations. - Yves Rychener; Xavier Renard; Djamé Seddah; Pascal Frossard; Marcin Detyniecki (CoRR, 2020)

How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. - Tom Vermeire; Thibault Laugel; Xavier Renard; David Martens; Marcin Detyniecki (PKDD/ECML Workshops (1), 2021)

Understanding Prediction Discrepancies in Machine Learning Classifiers. - Xavier Renard; Thibault Laugel; Marcin Detyniecki (CoRR, 2021)

On the overlooked issue of defining explanation objectives for local-surrogate explainers. - Rafael Poyiadzi; Xavier Renard; Thibault Laugel; Raúl Santos-Rodríguez; Marcin Detyniecki (CoRR, 2021)

Understanding surrogate explanations: the interplay between complexity, fidelity and coverage. - Rafael Poyiadzi; Xavier Renard; Thibault Laugel; Raúl Santos-Rodríguez; Marcin Detyniecki (CoRR, 2021)

On the Granularity of Explanations in Model Agnostic NLP Interpretability. - Yves Rychener; Xavier Renard; Djamé Seddah; Pascal Frossard; Marcin Detyniecki (PKDD/ECML Workshops (1), 2022)

Dynamic Interpretability for Model Comparison via Decision Rules. - Adam Rida; Marie-Jeanne Lesot; Xavier Renard; Christophe Marsala (PKDD/ECML Workshops (1), 2023)

Post-processing fairness with minimal changes. - Federico Di Gennaro; Thibault Laugel; Vincent Grari; Xavier Renard; Marcin Detyniecki (CoRR, 2024)

Understanding prediction discrepancies in classification. - Xavier Renard; Thibault Laugel; Marcin Detyniecki (Mach. Learn., 2024)

Metric assessment protocol in the context of answer fluctuation on MCQ tasks. - Ekaterina Goliakova; Xavier Renard; Marie-Jeanne Lesot; Thibault Laugel; Christophe Marsala; Marcin Detyniecki (CoRR, 2025)