Bertrand Charpentier

According to our database, Bertrand Charpentier authored at least 26 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Uncertainty Estimation for Independent and Non-Independent Data.

Bertrand Charpentier

PhD thesis, 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

Structurally Prune Anything: Any Architecture, Any Framework, Any Time.

Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier

CoRR, 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

CoRR, 2024

Uncertainty for Active Learning on Graphs.

Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Adversarial Training for Graph Neural Networks.

Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, 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

Training, Architecture, and Prior for Deterministic Uncertainty Methods.

Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann

CoRR, 2023

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions.

Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Edge Directionality Improves Learning on Heterophilic Graphs.

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Uncertainty Estimation for Molecules: Desiderata and Methods.

Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

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

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning.

Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann

CoRR, 2022

Winning the Lottery Ahead of Time: Efficient Early Network Pruning.

John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2022

End-to-End Learning of Probabilistic Hierarchies on Graphs.

Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann

Proceedings of the Tenth International Conference on Learning Representations, 2022

Differentiable DAG Sampling.

Bertrand Charpentier, Simon Kibler, Stephan Günnemann

Proceedings of the Tenth International Conference on Learning Representations, 2022

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions.

Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

On Out-of-distribution Detection with Energy-based Models.

Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

CoRR, 2021

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification.

Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Scikit-network: Graph Analysis in Python.

Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier

J. Mach. Learn. Res., 2020

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts.

Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Uncertainty on Asynchronous Time Event Prediction.

Bertrand Charpentier, Marin Bilos, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering.

Bertrand Charpentier, Thomas Bonald

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Learning Graph Representations by Dendrograms.

Thomas Bonald, Bertrand Charpentier

CoRR, 2018

Hierarchical Graph Clustering using Node Pair Sampling.

Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou

CoRR, 2018