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Bertrand Charpentier
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B Charpentier, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 33, 2020
1362020
Hierarchical graph clustering using node pair sampling
T Bonald, B Charpentier, A Galland, A Hollocou
arXiv preprint arXiv:1806.01664, 2018
622018
Graph posterior network: Bayesian predictive uncertainty for node classification
M Stadler, B Charpentier, S Geisler, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 34, 18033-18048, 2021
612021
Scikit-network: Graph analysis in python
T Bonald, N De Lara, Q Lutz, B Charpentier
Journal of Machine Learning Research 21 (185), 1-6, 2020
602020
Natural posterior network: Deep bayesian predictive uncertainty for exponential family distributions
B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann
International Conference on Learning Representations, 2021
50*2021
Evaluating robustness of predictive uncertainty estimation: Are Dirichlet-based models reliable?
AK Kopetzki, B Charpentier, D Zügner, S Giri, S Günnemann
International Conference on Machine Learning, 5707-5718, 2021
422021
Uncertainty on asynchronous time event prediction
M Biloš, B Charpentier, S Günnemann
Advances in Neural Information Processing Systems 32, 2019
332019
Differentiable DAG Sampling
B Charpentier, S Kibler, S Günnemann
International Conference on Learning Representations, 2022
292022
Winning the lottery ahead of time: Efficient early network pruning
J Rachwan, D Zügner, B Charpentier, S Geisler, M Ayle, S Günnemann
International Conference on Machine Learning, 18293-18309, 2022
192022
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
B Charpentier, R Senanayake, M Kochenderfer, S Günnemann
Distribution-Free Uncertainty Quantification Workshop (DFUQ - ICML), 2022
152022
On out-of-distribution detection with energy-based models
S Elflein, B Charpentier, D Zügner, S Günnemann
Uncertainty and Robustness in Deep Learning - ICML Workshop, 2021
152021
Edge directionality improves learning on heterophilic graphs
E Rossi, B Charpentier, F Di Giovanni, F Frasca, S Günnemann, ...
arXiv preprint arXiv:2305.10498, 2023
112023
Adversarial training for graph neural networks: Pitfalls, solutions, and new directions
L Gosch, S Geisler, D Sturm, B Charpentier, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 36, 2024
102024
On the Robustness and Anomaly Detection of Sparse Neural Networks
M Ayle, B Charpentier, J Rachwan, D Zügner, S Geisler, S Günnemann
Sparsity in Neural Networks Workshop (SNN), 2022
62022
Uncertainty estimation for molecules: Desiderata and methods
T Wollschläger, N Gao, B Charpentier, MA Ketata, S Günnemann
International Conference on Machine Learning, 37133-37156, 2023
52023
Training, Architecture, and Prior for Deterministic Uncertainty Methods
B Charpentier, C Zhang, S Günnemann
Pitfalls of limited data and computation for Trustworthy ML Workshop …, 2023
52023
Hierarchical graph clustering using node pair sampling. arXiv
T Bonald, B Charpentier, A Galland, A Hollocou
arXiv preprint arXiv:1806.01664, 2018
52018
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
J Getzner, B Charpentier, S Günnemann
Tackling Climate Change with Machine Learning: Global Perspectives and Local …, 2023
42023
Tree sampling divergence: an information-theoretic metric for hierarchical graph clustering
B Charpentier, T Bonald
IJCAI-19, 2019
42019
End-to-end learning of probabilistic hierarchies on graphs
D Zügner, B Charpentier, M Ayle, S Geringer, S Günnemann
International Conference on Learning Representations, 2021
32021
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Articles 1–20