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ORCIDXavier Renard
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2020 – today
- 2024
[j1]Xavier Renard, Thibault Laugel, Marcin Detyniecki:
Understanding prediction discrepancies in classification. Mach. Learn. 113(10): 7997-8026 (2024)- [i14]Federico Di Gennaro, Thibault Laugel, Vincent Grari, Xavier Renard, Marcin Detyniecki:
Post-processing fairness with minimal changes. CoRR abs/2408.15096 (2024) - 2023
- [i13]Adam Rida, Marie-Jeanne Lesot, Xavier Renard, Christophe Marsala:
Dynamic Interpretability for Model Comparison via Decision Rules. CoRR abs/2309.17095 (2023) - 2022
- [c8]Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki:
On the Granularity of Explanations in Model Agnostic NLP Interpretability. PKDD/ECML Workshops (1) 2022: 498-512 - 2021
- [c7]Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki:
How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. PKDD/ECML Workshops (1) 2021: 521-533 - [i12]Xavier Renard, Thibault Laugel, Marcin Detyniecki:
Understanding Prediction Discrepancies in Machine Learning Classifiers. CoRR abs/2104.05467 (2021) - [i11]Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raúl Santos-Rodríguez, Marcin Detyniecki:
On the overlooked issue of defining explanation objectives for local-surrogate explainers. CoRR abs/2106.05810 (2021) - [i10]Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raúl Santos-Rodríguez, Marcin Detyniecki:
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage. CoRR abs/2107.04309 (2021) - [i9]Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki:
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. CoRR abs/2107.04427 (2021) - 2020
- [i8]Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki:
Sentence-Based Model Agnostic NLP Interpretability. CoRR abs/2012.13189 (2020) - [i7]Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki:
QUACKIE: A NLP Classification Task With Ground Truth Explanations. CoRR abs/2012.13190 (2020)
2010 – 2019
- 2019
- [c6]Thibault Laugel
, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations. IJCAI 2019: 2801-2807 - [c5]Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
Unjustified Classification Regions and Counterfactual Explanations in Machine Learning. ECML/PKDD (2) 2019: 37-54 - [c4]Maël Guillemé, Simon Malinowski, Romain Tavenard, Xavier Renard:
Localized Random Shapelets. AALTD@PKDD/ECML 2019: 85-97 - [i6]Xavier Renard, Nicolas Woloszko, Jonathan Aigrain, Marcin Detyniecki:
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees. CoRR abs/1906.01297 (2019) - [i5]Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations. CoRR abs/1907.09294 (2019) - [i4]Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, Marcin Detyniecki:
Imperceptible Adversarial Attacks on Tabular Data. CoRR abs/1911.03274 (2019) - 2018
- [c3]Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
Comparison-Based Inverse Classification for Interpretability in Machine Learning. IPMU (1) 2018: 100-111 - [c2]Xavier Renard, Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Marcin Detyniecki:
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense. Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML 2018: 41-47 - [i3]Thibault Laugel, Xavier Renard, Marie-Jeanne Lesot, Christophe Marsala, Marcin Detyniecki:
Defining Locality for Surrogates in Post-hoc Interpretablity. CoRR abs/1806.07498 (2018) - [i2]Xavier Renard, Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Marcin Detyniecki:
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense. CoRR abs/1809.02397 (2018) - 2017
- [b1]Xavier Renard:
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). Pierre and Marie Curie University, Paris, France, 2017 - [i1]Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
Inverse Classification for Comparison-based Interpretability in Machine Learning. CoRR abs/1712.08443 (2017) - 2015
- [c1]Xavier Renard, Maria Rifqi, Walid Erray, Marcin Detyniecki:
Random-shapelet: An algorithm for fast shapelet discovery. DSAA 2015: 1-10
Coauthor Index
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