Hierarchical Graph Clustering using Node Pair Sampling. - Thomas Bonald; Bertrand Charpentier; Alexis Galland; Alexandre Hollocou (CoRR, 2018)

Learning Graph Representations by Dendrograms. - Thomas Bonald; Bertrand Charpentier (CoRR, 2018)

Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering. - Bertrand Charpentier; Thomas Bonald (IJCAI, 2019)

Uncertainty on Asynchronous Time Event Prediction. - Bertrand Charpentier; Marin Bilos; Stephan Günnemann (NeurIPS, 2019)

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. - Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2020)

Scikit-network: Graph Analysis in Python. - Thomas Bonald; Nathan de Lara; Quentin Lutz; Bertrand Charpentier (J. Mach. Learn. Res., 2020)

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? - Anna-Kathrin Kopetzki; Bertrand Charpentier; Daniel Zügner; Sandhya Giri; Stephan Günnemann (ICML, 2021)

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. - Maximilian Stadler; Bertrand Charpentier; Simon Geisler; Daniel Zügner; Stephan Günnemann (NeurIPS, 2021)

On Out-of-distribution Detection with Energy-based Models. - Sven Elflein; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2021)

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. - Bertrand Charpentier; Oliver Borchert; Daniel Zügner; Simon Geisler; Stephan Günnemann (ICLR, 2022)

Differentiable DAG Sampling. - Bertrand Charpentier; Simon Kibler; Stephan Günnemann (ICLR, 2022)

End-to-End Learning of Probabilistic Hierarchies on Graphs. - Daniel Zügner; Bertrand Charpentier; Morgane Ayle; Sascha Geringer; Stephan Günnemann (ICLR, 2022)

Winning the Lottery Ahead of Time: Efficient Early Network Pruning. - John Rachwan; Daniel Zügner; Bertrand Charpentier; Simon Geisler; Morgane Ayle; Stephan Günnemann (ICML, 2022)

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. - Bertrand Charpentier; Ransalu Senanayake; Mykel J. Kochenderfer; Stephan Günnemann (CoRR, 2022)

On the Robustness and Anomaly Detection of Sparse Neural Networks. - Morgane Ayle; Bertrand Charpentier; John Rachwan; Daniel Zügner; Simon Geisler; Stephan Günnemann (CoRR, 2022)

Uncertainty Estimation for Molecules: Desiderata and Methods. - Tom Wollschläger; Nicholas Gao; Bertrand Charpentier; Mohamed Amine Ketata; Stephan Günnemann (ICML, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2023)

Training, Architecture, and Prior for Deterministic Uncertainty Methods. - Bertrand Charpentier; Chenxiang Zhang; Stephan Günnemann (CoRR, 2023)

Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. - Johannes Getzner; Bertrand Charpentier; Stephan Günnemann (CoRR, 2023)

Adversarial Training for Graph Neural Networks. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2023)

Uncertainty for Active Learning on Graphs. - Dominik Fuchsgruber; Tom Wollschläger; Bertrand Charpentier; Antonio Oroz; Stephan Günnemann (ICML, 2024)

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. - Rayen Dhahri; Alexander Immer; Bertrand Charpentier; Stephan Günnemann; Vincent Fortuin (NeurIPS, 2024)

Expected Probabilistic Hierarchies. - Marcel Kollovieh; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2024)

Structurally Prune Anything: Any Architecture, Any Framework, Any Time. - Xun Wang; John Rachwan; Stephan Günnemann; Bertrand Charpentier (CoRR, 2024)

Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. - Florence Regol; Joud Chataoui; Bertrand Charpentier; Mark Coates; Pablo Piantanida; Stephan Günnemann (CoRR, 2024)

Uncertainty Estimation for Independent and Non-Independent Data. - Bertrand Charpentier (2024)