| Inverse classification for comparison-based interpretability in machine learning T Laugel, MJ Lesot, C Marsala, X Renard, M Detyniecki arXiv preprint arXiv:1712.08443, 2017 | 251* | 2017 |
| The dangers of post-hoc interpretability: Unjustified counterfactual explanations T Laugel, MJ Lesot, C Marsala, X Renard, M Detyniecki arXiv preprint arXiv:1907.09294, 2019 | 250 | 2019 |
| Defining locality for surrogates in post-hoc interpretablity T Laugel, X Renard, MJ Lesot, C Marsala, M Detyniecki arXiv preprint arXiv:1806.07498, 2018 | 119 | 2018 |
| Imperceptible adversarial attacks on tabular data V Ballet, X Renard, J Aigrain, T Laugel, P Frossard, M Detyniecki arXiv preprint arXiv:1911.03274, 2019 | 107 | 2019 |
| Random-shapelet: an algorithm for fast shapelet discovery X Renard, M Rifqi, W Erray, M Detyniecki 2015 IEEE international conference on data science and advanced analytics …, 2015 | 58 | 2015 |
| Unjustified classification regions and counterfactual explanations in machine learning T Laugel, MJ Lesot, C Marsala, X Renard, M Detyniecki Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 33 | 2020 |
| How to choose an explainability method? Towards a methodical implementation of XAI in practice T Vermeire, T Laugel, X Renard, D Martens, M Detyniecki Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 23 | 2021 |
| EAST representation: fast discriminant temporal patterns discovery from time series X Renard, M Rifqi, G Fricout, M Detyniecki ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, 2016 | 15* | 2016 |
| Concept tree: High-level representation of variables for more interpretable surrogate decision trees X Renard, N Woloszko, J Aigrain, M Detyniecki arXiv preprint arXiv:1906.01297, 2019 | 13 | 2019 |
| A reference architecture for quality improvement in steel production D Arnu, E Yaqub, C Mocci, V Colla, M Neuer, G Fricout, X Renard, ... Data Science–Analytics and Applications: Proceedings of the 1st …, 2017 | 12 | 2017 |
| Understanding prediction discrepancies in machine learning classifiers X Renard, T Laugel, M Detyniecki arXiv preprint arXiv:2104.05467, 2021 | 11 | 2021 |
| Localized random shapelets M Guilleme, S Malinowski, R Tavenard, X Renard Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop …, 2020 | 11 | 2020 |
| Detecting potential local adversarial examples for human-interpretable defense X Renard, T Laugel, MJ Lesot, C Marsala, M Detyniecki ECML PKDD 2018 Workshops: Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe …, 2019 | 11 | 2019 |
| On the overlooked issue of defining explanation objectives for local-surrogate explainers R Poyiadzi, X Renard, T Laugel, R Santos-Rodriguez, M Detyniecki arXiv preprint arXiv:2106.05810, 2021 | 10 | 2021 |
| Time series representation for classification: a motif-based approach X Renard Université Pierre et Marie Curie-Paris VI, 2017 | 8 | 2017 |
| Understanding surrogate explanations: the interplay between complexity, fidelity and coverage R Poyiadzi, X Renard, T Laugel, R Santos-Rodriguez, M Detyniecki arXiv preprint arXiv:2107.04309, 2021 | 6 | 2021 |
| Sentence-based model agnostic nlp interpretability Y Rychener, X Renard, D Seddah, P Frossard, M Detyniecki arXiv preprint arXiv:2012.13189, 2020 | 5 | 2020 |
| On the Granularity of Explanations in Model Agnostic NLP Interpretability Y Rychener, X Renard, D Seddah, P Frossard, M Detyniecki Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 3 | 2022 |
| Quackie: A NLP classification task with ground truth explanations Y Rychener, X Renard, D Seddah, P Frossard, M Detyniecki arXiv preprint arXiv:2012.13190, 2020 | 3 | 2020 |
| Post-processing fairness with minimal changes F Di Gennaro, T Laugel, V Grari, X Renard, M Detyniecki arXiv preprint arXiv:2408.15096, 2024 | 1 | 2024 |
Marcin Detyniecki, Ph.D.Head of R&D, @AXA + Associate researcher, @Sorbonne_Université, @CNRS, @Polish_Academy_of_ScienceVerified email at axa.com
