People


Bertrand Charpentier

Bertrand Charpentier
Institution: 
■ Institut Mines-Telecom
Office: 
50
Groups: 
Former intern

Articles

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Uncertainty for Active Learning on Graphs,
Dominik Fuchsgruber, Tom Wollschlager, Bertrand Charpentier, Antonio Oroz, Stephan Gunnemann,
ICML 2024
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Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood,
Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Gunnemann, Vincent Fortuin,
NeurIPS 2024
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Expected Probabilistic Hierarchies,
Marcel Kollovieh, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann,
NeurIPS 2024
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Structurally Prune Anything: Any Architecture, Any Framework, Any Time,
Xun Wang, John Rachwan, Stephan Gunnemann, Bertrand Charpentier,
CoRR 2024
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Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE,
Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Gunnemann,
CoRR 2024
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Uncertainty Estimation for Molecules: Desiderata and Methods,
Tom Wollschlager, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Gunnemann,
ICML 2023
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Edge Directionality Improves Learning on Heterophilic Graphs,
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Gunnemann, Michael Bronstein,
LoG 2023
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Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions,
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann,
NeurIPS 2023
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Training, Architecture, and Prior for Deterministic Uncertainty Methods,
Bertrand Charpentier, Chenxiang Zhang, Stephan Gunnemann,
CoRR 2023
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Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models,
Johannes Getzner, Bertrand Charpentier, Stephan Gunnemann,
CoRR 2023
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Adversarial Training for Graph Neural Networks,
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann,
CoRR 2023
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Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions,
Bertrand Charpentier, Oliver Borchert, Daniel Zugner, Simon Geisler, Stephan Gunnemann,
ICLR 2022
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Differentiable DAG Sampling,
Bertrand Charpentier, Simon Kibler, Stephan Gunnemann,
ICLR 2022
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End-to-End Learning of Probabilistic Hierarchies on Graphs,
Daniel Zugner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Gunnemann,
ICLR 2022
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Winning the Lottery Ahead of Time: Efficient Early Network Pruning,
John Rachwan, Daniel Zugner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Gunnemann,
ICML 2022
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Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning,
Bertrand Charpentier, Ransalu Senanayake, Mykel Kochenderfer, Stephan Gunnemann,
CoRR 2022
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On the Robustness and Anomaly Detection of Sparse Neural Networks,
Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zugner, Simon Geisler, Stephan Gunnemann,
CoRR 2022
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Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?,
Anna Kathrin Kopetzki, Bertrand Charpentier, Daniel Zugner, Sandhya Giri, Stephan Gunnemann,
ICML 2021
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Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification,
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zugner, Stephan Gunnemann,
NeurIPS 2021
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On Out-of-distribution Detection with Energy-based Models,
Sven Elflein, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann,
CoRR 2021
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Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts,
Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann,
NeurIPS 2020
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Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering,
Bertrand Charpentier, Thomas Bonald ■,
IJCAI 2019, Macao, China
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Uncertainty on Asynchronous Time Event Prediction,
Bertrand Charpentier, Marin Bilos, Stephan Gunnemann,
NeurIPS 2019
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Learning Graph Representations by Dendrograms,
Thomas Bonald ■, Bertrand Charpentier ■,
arXiv.org 2018
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Hierarchical Graph Clustering using Node Pair Sampling,
Thomas Bonald ■, Bertrand Charpentier ■, Alexis Galland ■, Alexandre Hollocou ■,
CoRR 2018, London, United Kingdom

Journal article

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Scikit-network: Graph Analysis in Python,
Thomas Bonald ■, Nathan De Lara ■, Quentin Lutz ■, Bertrand Charpentier,
Journal of Machine Learning Research 2020
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