Bertrand Charpentier

Index Institution office homepage group 0 ■ Institut Mines-Telecom 50 None Former interns

s

			⇣	Uncertainty Estimation for Molecules: Desiderata and Methods, Tom Wollschlager, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Gunnemann, ICML 2023
			⇣	Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions, Bertrand Charpentier, Oliver Borchert, Daniel Zugner, Simon Geisler, Stephan Gunnemann, ICLR 2022
			⇣	Differentiable DAG Sampling, Bertrand Charpentier, Simon Kibler, Stephan Gunnemann, ICLR 2022
			⇣	End-to-End Learning of Probabilistic Hierarchies on Graphs, Daniel Zugner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Gunnemann, ICLR 2022
			⇣	Winning the Lottery Ahead of Time: Efficient Early Network Pruning, John Rachwan, Daniel Zugner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Gunnemann, ICML 2022
			⇣	Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?, Anna Kathrin Kopetzki, Bertrand Charpentier, Daniel Zugner, Sandhya Giri, Stephan Gunnemann, ICML 2021
			⇣	Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification, Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zugner, Stephan Gunnemann, NeurIPS 2021
			⇣	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann, NeurIPS 2020
		⇣	⇣	Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering, Bertrand Charpentier, Thomas Bonald ■, IJCAI 2019, Macao, China
			⇣	Uncertainty on Asynchronous Time Event Prediction, Bertrand Charpentier, Marin Bilos, Stephan Gunnemann, NeurIPS 2019

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

Misc

			⇣	Training, Architecture, and Prior for Deterministic Uncertainty Methods, Bertrand Charpentier, Chenxiang Zhang, Stephan Gunnemann, CoRR 2023
			⇣	Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models, Johannes Getzner, Bertrand Charpentier, Stephan Gunnemann, CoRR 2023
			⇣	Edge Directionality Improves Learning on Heterophilic Graphs, Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Gunnemann, Michael Bronstein, CoRR 2023
			⇣	Adversarial Training for Graph Neural Networks, Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann, CoRR 2023
			⇣	Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning, Bertrand Charpentier, Ransalu Senanayake, Mykel Kochenderfer, Stephan Gunnemann, CoRR 2022
			⇣	On the Robustness and Anomaly Detection of Sparse Neural Networks, Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zugner, Simon Geisler, Stephan Gunnemann, CoRR 2022
			⇣	On Out-of-distribution Detection with Energy-based Models, Sven Elflein, Bertrand Charpentier, Daniel Zugner, Stephan Gunnemann, CoRR 2021
		⇣	⇣	Learning Graph Representations by Dendrograms, Thomas Bonald ■, Bertrand Charpentier ■, arXiv.org 2018
		⇣	⇣	Hierarchical Graph Clustering using Node Pair Sampling, Thomas Bonald ■, Bertrand Charpentier ■, Alexis Galland ■, Alexandre Hollocou ■, CoRR 2018, London, United Kingdom