68 citations · 201 across the 13 of their papers we have counts for
15 papers
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
Thibault Laugel, Adulam Jeyasothy, Marie-Jeanne Lesot +2
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to…
TIGTEC : Token Importance Guided TExt Counterfactuals
Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau +1
Counterfactual examples explain a prediction by highlighting changes of instance that flip the outcome of a classifier. This paper proposes TIGTEC, an efficient and modular method…
Integrating Prior Knowledge in Post-hoc Explanations
Adulam Jeyasothy, Thibault Laugel, Marie-Jeanne Lesot +2
In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model. Integrating pr…
CG2A: Conceptual Graphs Generation Algorithm
Adam Faci, Marie-Jeanne Lesot, Claire Laudy
Conceptual Graphs (CGs) are a formalism to represent knowledge. However producing a CG database is complex. To the best of our knowledge, existing methods do not fully use the expr…
Fuzzy Conceptual Graphs: a comparative discussion
Adam Faci, Marie-Jeanne Lesot, Claire Laudy
Conceptual Graphs (CG) are a graph-based knowledge representation and reasoning formalism; fuzzy Conceptual Graphs (fCG) constitute an extension that enriches their expressiveness,…
cgSpan: Pattern Mining in Conceptual Graphs
Adam Faci, Marie-Jeanne Lesot, Claire Laudy
Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It extends the DMGM-GSM algori…
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +2
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the…
Issues with post-hoc counterfactual explanations: a discussion
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +1
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier. However, the…
ROHSA: Regularized Optimization for Hyper-Spectral Analysis - Application to phase separation of 21 cm data
Antoine Marchal, Marc-Antoine Miville-Deschenes, Francois Orieux +5
Extracting the multiphase structure of the neutral interstellar medium (ISM) is key to understand the star formation in galaxies. The radiative condensation of the diffuse warm neu…
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense
Xavier Renard, Thibault Laugel, Marie-Jeanne Lesot +2
Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval. Some people may be tempted to manipulate specific variables, such…
Defining Locality for Surrogates in Post-hoc Interpretablity
Thibault Laugel, Xavier Renard, Marie-Jeanne Lesot +2
Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individua…
Inverse Classification for Comparison-based Interpretability in Machine Learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +2
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, nei…
Overlapping Community Detection by Local Decentralised Vertex-centred Process
Maël Canu, Marie-Jeanne Lesot, Adrien Revault d'Allonnes
This paper focuses on the identification of overlapping communities, allowing nodes to simultaneously belong to several communities, in a decentralised way. To that aim it proposes…
Vertex-centred Method to Detect Communities in Evolving Networks
Maël Canu, Marie-Jeanne Lesot, Adrien Revault d'Allonnes
Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex…
Fast community structure local uncovering by independent vertex-centred process
Maël Canu, Marcin Detyniecki, Marie-Jeanne Lesot +1
This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only de…