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Adrien Bibal
Adrien Bibal
Principal Scientist, InferLink
Adresse e-mail validée de cuanschutz.edu
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Année
Interpretability of machine learning models and representations: an introduction.
A Bibal, B Frénay
ESANN, 77-82, 2016
1562016
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A de Streel, B Frénay
Artificial Intelligence and Law 29, 149–169, 2020
1542020
Is Attention Explanation? An Introduction to the Debate
A Bibal, R Cardon, D Alfter, R Souza Wilkens, X Wang, T François, ...
ACL, 3889-3900, 2022
542022
Recasting a Traditional Course into a MOOC by Means of a SPOC
S Combéfis, A Bibal, P Van Roy
European MOOCs Stakeholders Summit, 205-208, 2014
452014
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
A Barragán-Montero, A Bibal, MH Dastarac, C Draguet, G Valdés, ...
Physics in Medicine & Biology 67 (11), 11TR01, 2022
332022
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
V Delchevalerie, A Bibal, B Frénay, A Mayer
NeurIPS 34, 28772-28783, 2021
172021
Explaining t-SNE Embeddings Locally by Adapting LIME
A Bibal, VM Vu, G Nanfack, B Frénay
ESANN, 393-398, 2020
172020
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
162019
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
162018
Measuring Quality and Interpretability of Dimensionality Reduction Visualizations
A Bibal, B Frénay
ICLR Workshop on SafeML, 2019
122019
Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features
A Bibal, R Marion, B Frénay
ESANN, 537-542, 2018
122018
DT-SNE: t-SNE discrete visualizations as decision tree structures
A Bibal, V Delchevalerie, B Frénay
Neurocomputing 529, 101-112, 2023
112023
Impact of Legal Requirements on Explainability in Machine Learning
A Bibal, M Lognoul, A de Streel, B Frénay
ICML Workshop on Law & Machine Learning, 2020
92020
Linguistic Corpus Annotation for Automatic Text Simplification Evaluation
R Cardon, A Bibal, R Souza Wilkens, D Alfter, M Norré, A Müller, P Watrin, ...
EMNLP, 1842-1866, 2022
82022
IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees
A Bibal, A Clarinval, B Dumas, B Frénay
Array 11, 100080, 2021
82021
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
82016
Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization
MV Vu, A Bibal, B Frenay
IEEE Transactions on Artificial Intelligence 2 (3), 269-282, 2021
62021
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
62019
GanoDIP-GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case
A Bougaham, A Bibal, I Linden, B Frenay
ECML-PKDD Workshop on Learning with Imbalanced Domains: Theory and …, 2021
52021
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
52021
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