Bertrand Charpentier

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 35

Top Topics

Marginal Likelihood

Graph Representations

Kullback Leibler

Top Venues

Publications

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 (2024)
Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier
Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR (2024)
Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR (2024)
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
Uncertainty for Active Learning on Graphs. CoRR (2024)
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Adversarial Training for Graph Neural Networks. CoRR (2023)
Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR (2023)
Johannes Getzner, Bertrand Charpentier, Stephan Günnemann
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR (2023)
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. LoG (2023)
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR (2023)
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)
Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR (2022)
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML (2022)
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR (2022)
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
Differentiable DAG Sampling. ICLR (2022)
Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann
On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR (2022)
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
Differentiable DAG Sampling. CoRR (2022)
Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR (2022)
Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR (2022)
Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
On Out-of-distribution Detection with Energy-based Models. CoRR (2021)
Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR (2021)
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR (2021)
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS (2021)
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML (2021)
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS (2020)
Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier
Scikit-network: Graph Analysis in Python. CoRR (2020)
Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier
Scikit-network: Graph Analysis in Python. J. Mach. Learn. Res. 21 (2020)
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR (2020)
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR (2020)
Bertrand Charpentier, Marin Bilos, Stephan Günnemann
Uncertainty on Asynchronous Time Event Prediction. NeurIPS (2019)
Bertrand Charpentier, Thomas Bonald
Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering. IJCAI (2019)
Marin Bilos, Bertrand Charpentier, Stephan Günnemann
Uncertainty on Asynchronous Time Event Prediction. CoRR (2019)
Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou
Hierarchical Graph Clustering using Node Pair Sampling. CoRR (2018)
Thomas Bonald, Bertrand Charpentier
Learning Graph Representations by Dendrograms. CoRR (2018)