Explaining Local Discrepancies between Image Classification Models

Laugel T., Renard X., Detyniecki M.2022

XAI4CV workshop (CVPR 2022)

Understanding surrogate explanations: the interplay between complexity, fidelity and coverage

Poyiadzi R., Renard X., Laugel T., Santos-Rodriguez R., Detyniecki M.2021

arxiv preprint

How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Vermeire T., Laugel T., Renard X., Martens D., Detyniecki M.2021

ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining (ECML XKDD 2021)

On the overlooked issue of defining explanation objectives for local-surrogate explainers

Poyiadzi R., Renard X., Laugel T., Santos-Rodriguez R., Detyniecki M.2021

International Conference on Machine Learning (ICML) Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI

Understanding Prediction Discrepancies in Machine Learning Classifiers

Renard X., Laugel T., Detyniecki M.2021

arxiv preprint

Sentence-Based Model Agnostic NLP Interpretability

Rychener Y., Renard X., Seddah D., Frossard P., Detyniecki M. 2020

Github repository

QUACKIE: A NLP Classification Task With Ground Truth Explanations

Rychener Y., Renard X., Seddah D., Frossard P., Detyniecki M. 2020

Github repository

Benchmark's website of NLP interpretability methods

Local Post-hoc Interpretability for Black-box Classifiers

Laugel, T.2020

Ph.D. Thesis

Imperceptible Adversarial Attacks on Tabular Data

Ballet V., Renard X., Aigrain J., Laugel T., Frossard P., Detyniecki M.2019

NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy (Robust AI in FS 2019)

Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees

Renard X., Woloszko N., Aigrain J., Detyniecki M.2019

Bank of England and King's College London joint conference on Modelling with Big Data and Machine Learning: Interpretability and Model Uncertainty

The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations

Laugel, T., Lesot, M. J., Marsala, C., Renard, X., & Detyniecki, M.2019

International Joint Conference on Artificial Intelligence (IJCAI)

Github repository

Unjustified Classification Regions and Counterfactual Explanations In Machine Learning

Laugel, T., Lesot, M. J., Marsala, C., Renard, X., & Detyniecki, M.2019

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees

Renard X., Woloszko N., Aigrain J., Detyniecki M.2019

International Conference on Machine Learning (ICML) Workshop on Human In the Loop Learning (HILL)

Issues with post-hoc counterfactual explanations: a discussion

Laugel, T., Lesot, M.-J., Marsala, C., Detyniecki, M.2019

International Conference on Machine Learning (ICML) Workshop on Human In the Loop Learning (HILL)

Detecting Potential Local Adversarial Examples for Human-Interpretable Defense.

Renard X., Laugel T., Lesot MJ., Marsala C., Detyniecki M.2018

European Conference on Machine Learning (ECML/PKDD) Workshop on Recent Advances in Adversarial Machine Learning (Nemesis)

Defining Locality for Surrogates in Post-hoc Interpretablity.

Laugel T., Renard X., Lesot MJ., Marsala C., Detyniecki M.2018

International Conference on Machine Learning (ICML) Workshop on Human Interpretability in Machine Learning (WHI 2018)

Comparison-based inverse classification for interpretability in machine learning

Laugel, T., Lesot, M. J., Marsala, C., Renard, X., & Detyniecki, M.2018

International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

Github repository

Inverse Classification for Comparison-based Interpretability in Machine Learning

Laugel, T., Lesot, M. J., Marsala, C., Renard, X., & Detyniecki, M.2017

arXiv preprint arXiv:1712.08443