LAUGEL Thibault

PhD student()
Team : LFI
Arrival date : 02/01/2017
Localisation : Campus Pierre et Marie Curie

Sorbonne Université - LIP6

Tel: +33 1 44 27 87 26, Thibault.Laugel (at) nulllip6.fr
http://webia.lip6.fr/~laugel/

Supervision : Christophe MARSALA
Co-supervision : LESOT Marie-Jeanne, DETYNIECKI Marcin

Interpretability for human-friendly machine learning models

In this thesis, we consider the problem of generating predictive explanations for a supervised classification model. The paradigm considered, called "post-hoc", studies the particular case where the model to be explained is treated as a "black box", i.e. no information is available about the classifier nor the data used to train it. Although practical since it allows a more flexible use (the same explanation system can be applied to any classification algorithm), the question arises as to the link between the generated explanations and the training data, to which the system did not have access. In this context, we are interested in different problems that these systems may face: the locality of the explanations, their link with the truth on the ground, and their stability. This analysis is performed through the study of a particular type of approach: counterfactuals, which aim at identifying the minimum perturbation to apply to an observation in order to change the class predicted by the model.

2018-2019 Publications

Conference papers

2019
- Th. Laugel, M.‑J. Lesot, Ch. Marsala, M. Detyniecki : “Issues with post-hoc counterfactual explanations: a discussion”, ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA (2019)
- Th. Laugel, M.‑J. Lesot, Ch. Marsala, X. Renard, M. Detyniecki : “The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations”, Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19, Macao, Macau SAR China, pp. 2801-2807, (International Joint Conferences on Artificial Intelligence Organization) (2019)
- Th. Laugel, M.‑J. Lesot, Ch. Marsala, X. Renard, M. Detyniecki : “Unjustified Classification Regions and Counterfactual Explanations In Machine Learning”, ECML-PKDD, Würzburg, Germany (2019)
2018
- Th. Laugel, M.‑J. Lesot, Ch. Marsala, X. Renard, M. Detyniecki : “Comparison-based Inverse Classification for Interpretability in Machine Learning”, 17^th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2018), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, Cadix, Spain, pp. 100-111, (Springer Verlag) (2018)
- Th. Laugel, X. Renard, M.‑J. Lesot, Ch. Marsala, M. Detyniecki : “Defining Locality for Surrogates in Post-hoc Interpretablity”, Workshop on Human Interpretability for Machine Learning (WHI) - International Conference on Machine Learning (ICML), Stockholm, Sweden (2018)
- X. Renard, Th. Laugel, M.‑J. Lesot, Ch. Marsala, M. Detyniecki : “Detecting Potential Local Adversarial Examples for Human-Interpretable Defense”, Workshop on Recent Advances in Adversarial Learning (Nemesis) of the European Conference on Machine Learning and Principles of Practice of Knowledge Discovery in Databases (ECML-PKDD), Dublin, Ireland (2018)