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ORCIDBertrand Charpentier
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2020 – today
- 2024
[b1]Bertrand Charpentier:
Uncertainty Estimation for Independent and Non-Independent Data. Technical University of Munich, Germany, 2024- [c13]Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann:
Uncertainty for Active Learning on Graphs. ICML 2024 - [i22]Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin:
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR abs/2402.15978 (2024) - [i21]Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier:
Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR abs/2403.18955 (2024) - [i20]Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann:
Uncertainty for Active Learning on Graphs. CoRR abs/2405.01462 (2024) - [i19]Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann:
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR abs/2406.14404 (2024) - 2023
- [c12]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML 2023: 37133-37156 - [c11]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. LoG 2023: 25 - [c10]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS 2023 - [i18]Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann:
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR abs/2303.05796 (2023) - [i17]Johannes Getzner, Bertrand Charpentier, Stephan Günnemann:
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR abs/2304.00897 (2023) - [i16]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR abs/2305.10498 (2023) - [i15]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR abs/2306.14916 (2023) - [i14]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks. CoRR abs/2306.15427 (2023) - 2022
- [c9]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR 2022 - [c8]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. ICLR 2022 - [c7]Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann:
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR 2022 - [c6]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML 2022: 18293-18309 - [i13]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. CoRR abs/2203.08509 (2022) - [i12]Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann:
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR abs/2206.01558 (2022) - [i11]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR abs/2206.10451 (2022) - [i10]Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann:
On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR abs/2207.04227 (2022) - 2021
- [c5]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021: 5707-5718 - [c4]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS 2021: 18033-18048 - [i9]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR abs/2105.04471 (2021) - [i8]Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
On Out-of-distribution Detection with Energy-based Models. CoRR abs/2107.08785 (2021) - [i7]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR abs/2110.14012 (2021) - 2020
- [j1]Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier:
Scikit-network: Graph Analysis in Python. J. Mach. Learn. Res. 21: 185:1-185:6 (2020) - [c3]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS 2020 - [i6]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR abs/2006.09239 (2020) - [i5]Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier:
Scikit-network: Graph Analysis in Python. CoRR abs/2009.07660 (2020) - [i4]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR abs/2010.14986 (2020)
2010 – 2019
- 2019
- [c2]Bertrand Charpentier, Thomas Bonald:
Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering. IJCAI 2019: 2067-2073 - [c1]Bertrand Charpentier, Marin Bilos, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. NeurIPS 2019: 12831-12840 - [i3]Marin Bilos, Bertrand Charpentier, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. CoRR abs/1911.05503 (2019) - 2018
- [i2]Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou:
Hierarchical Graph Clustering using Node Pair Sampling. CoRR abs/1806.01664 (2018) - [i1]Thomas Bonald, Bertrand Charpentier:
Learning Graph Representations by Dendrograms. CoRR abs/1807.05087 (2018)
Coauthor Index
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last updated on 2024-09-13 00:44 CEST by the dblp team
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