Adrien Bibal
Zitiert von
Zitiert von
Interpretability of machine learning models and representations: an introduction.
A Bibal, B Frénay
ESANN, 77-82, 2016
Recasting a Traditional Course into a MOOC by Means of a SPOC
S Combéfis, A Bibal, P Van Roy
Proceedings of the European MOOCs Stakeholders Summit, 205-208, 2014
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A de Streel, B Frénay
Artificial Intelligence and Law 29, 149–169, 2020
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables
R Marion, A Bibal, B Frénay
Neurocomputing 342, 83-96, 2019
ML + FV = ? A Survey on the Application of Machine Learning to Formal Verification
M Amrani, L Lúcio, A Bibal
arXiv preprint arXiv:1806.03600, 2018
Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features
A Bibal, R Marion, B Frénay
ESANN, 537-542, 2018
Measuring Quality and Interpretability of Dimensionality Reduction Visualizations
A Bibal, B Frénay
SafeML ICLR Workshop, 2019
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
A Bibal, B Frénay
NIPS Workshop on Interpretable Machine Learning in Complex Systems, 2016
Explaining t-SNE Embeddings Locally by Adapting LIME
A Bibal, VM Vu, G Nanfack, B Frénay
ESANN, 393-398, 2020
Impact of Legal Requirements on Explainability in Machine Learning
A Bibal, M Lognoul, A de Streel, B Frénay
ICML Law & Machine Learning Workshop, 2020
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation
A Bibal, R Marion, R von Sachs, B Frénay
Neurocomputing 453, 109-118, 2021
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
C Morariu, A Bibal, R Cutura, B Frénay, M Sedlmair
arXiv preprint arXiv:2105.09275, 2021
AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
A Bibal, T Bouadi, B Frénay, L Galárraga, J Oramas
Proceedings of the 29th ACM International Conference on Information …, 2020
Explaining the Black Box: when Law Controls AI
A de Streel, A Bibal, B Frénay, M Lognoul
CERRE, 2020
User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning
A Bibal, B Dumas, B Frénay
EGC Workshop on Advances in Interpretable Machine Learning and Artificial …, 2019
Introduction to Interpretability in Machine Learning
A Bibal, B Frénay
BENELEARN 2016, 2016
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