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ORCIDAdrien Bibal
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Books and Theses
- 2020
[b1]Adrien Bibal:
Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction. University of Namur, Belgium, 2020
Journal Articles
- 2024
- [j9]Adrien Bibal, Nourah M. Salem, Rémi Cardon, Elizabeth K. White, Daniel E. Acuna, Robin Burke, Lawrence E. Hunter:
RecSOI: recommending research directions using statements of ignorance. J. Biomed. Semant. 15(1): 2 (2024) - 2023
- [j8]Adrien Bibal
, Valentin Delchevalerie, Benoît Frénay:
DT-SNE: t-SNE discrete visualizations as decision tree structures. Neurocomputing 529: 101-112 (2023) - [j7]Cristina Morariu, Adrien Bibal, René Cutura, Benoît Frénay, Michael Sedlmair:
Predicting User Preferences of Dimensionality Reduction Embedding Quality. IEEE Trans. Vis. Comput. Graph. 29(1): 745-755 (2023) - 2022
- [j6]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey. IEEE Trans. Artif. Intell. 3(6): 944-962 (2022) - 2021
- [j5]Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay:
Legal requirements on explainability in machine learning. Artif. Intell. Law 29(2): 149-169 (2021) - [j4]Adrien Bibal, Antoine Clarinval, Bruno Dumas, Benoît Frénay:
IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees. Array 11: 100080 (2021) - [j3]Adrien Bibal, Rebecca Marion, Rainer von Sachs, Benoît Frénay:
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation. Neurocomputing 453: 109-118 (2021) - [j2]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization. IEEE Trans. Artif. Intell. 2(3): 269-282 (2021) - 2019
- [j1]Rebecca Marion, Adrien Bibal, Benoît Frénay:
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables. Neurocomputing 342: 83-96 (2019)
Conference and Workshop Papers
- 2023
- [c14]Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Patrick Watrin, Thomas François:
Annotation Linguistique pour l'Évaluation de la Simplification Automatique de Textes. CORIA-TALN (4) 2023: 35-48 - 2022
- [c13]Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin:
Is Attention Explanation? An Introduction to the Debate. ACL (1) 2022: 3889-3900 - [c12]Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas:
AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence. CIKM 2022: 5160 - [c11]Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Patrick Watrin, Thomas François:
Linguistic Corpus Annotation for Automatic Text Simplification Evaluation. EMNLP 2022: 1842-1866 - [c10]Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin:
L'Attention est-elle de l'Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate ). TALN-RECITAL 2022: 447-449 - 2021
- [c9]Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal, Benoît Frénay:
Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics. IJCNN 2021: 1-8 - [c8]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
iPMDS: Interactive Probabilistic Multidimensional Scaling. IJCNN 2021: 1-8 - [c7]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
HCt-SNE: Hierarchical Constraints with t-SNE. IJCNN 2021: 1-8 - [c6]Arnaud Bougaham, Adrien Bibal, Isabelle Linden, Benoît Frénay:
GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case. LIDTA@ECML/PKDD 2021: 104-117 - [c5]Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, Alexandre Mayer:
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks. NeurIPS 2021: 28772-28783 - 2020
- [c4]Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas:
AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. CIKM 2020: 3529-3530 - [c3]Adrien Bibal, Viet Minh Vu, Géraldin Nanfack, Benoît Frénay:
Explaining t-SNE Embeddings Locally by Adapting LIME. ESANN 2020: 393-398 - 2018
- [c2]Adrien Bibal, Rebecca Marion, Benoît Frénay:
Finding the most interpretable MDS rotation for sparse linear models based on external features. ESANN 2018 - 2016
- [c1]Adrien Bibal, Benoît Frénay:
Interpretability of machine learning models and representations: an introduction. ESANN 2016
Editorship
- 2021
- [e2]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e1]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents]
Informal and Other Publications
- 2024
- [i6]Steven Fincke, Adrien Bibal, Elizabeth Boschee:
Granting GPT-4 License and Opportunity: Enhancing Accuracy and Confidence Estimation for Few-Shot Event Detection. CoRR abs/2408.00914 (2024) - 2023
- [i5]Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal, Benoît Frénay:
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks. CoRR abs/2304.09214 (2023) - 2021
- [i4]Cristina Morariu, Adrien Bibal, René Cutura, Benoît Frénay, Michael Sedlmair:
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality. CoRR abs/2105.09275 (2021) - 2020
- [i3]Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay:
Impact of Legal Requirements on Explainability in Machine Learning. CoRR abs/2007.05479 (2020) - 2018
- [i2]Moussa Amrani, Levi Lúcio, Adrien Bibal:
ML + FV = ♡? A Survey on the Application of Machine Learning to Formal Verification. CoRR abs/1806.03600 (2018) - 2016
- [i1]Adrien Bibal, Benoît Frénay:
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. CoRR abs/1611.06175 (2016)
Coauthor Index
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