Bertrand Charpentier

40 papers A* 14Journal 24Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation. CoRR Johanna Sommer, John Rachwan, Nils Fleischmann, Stephan Günnemann, Bertrand Charpentier
2024	A*	`conf`	Expected Probabilistic Hierarchies. NeurIPS Marcel Kollovieh, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2024	J	`jnl`	Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann
2024	J	`jnl`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. NeurIPS Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	J	`jnl`	Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier
2024	—	`—`	Uncertainty Estimation for Independent and Non-Independent Data. Bertrand Charpentier
2024	A*	`conf`	Uncertainty for Active Learning on Graphs. ICML Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
2024	J	`jnl`	Uncertainty for Active Learning on Graphs. CoRR Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
2023	J	`jnl`	Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR Johannes Getzner, Bertrand Charpentier, Stephan Günnemann
2023	J	`jnl`	Adversarial Training for Graph Neural Networks. CoRR Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2023	A*	`conf`	Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2023	—	`conf`	Edge Directionality Improves Learning on Heterophilic Graphs. LoG Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	J	`jnl`	Edge Directionality Improves Learning on Heterophilic Graphs. CoRR Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
2023	J	`jnl`	Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann
2023	A*	`conf`	Uncertainty Estimation for Molecules: Desiderata and Methods. ICML Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
2023	J	`jnl`	Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
2022	A*	`conf`	Differentiable DAG Sampling. ICLR Bertrand Charpentier, Simon Kibler, Stephan Günnemann
2022	J	`jnl`	Differentiable DAG Sampling. CoRR Bertrand Charpentier, Simon Kibler, Stephan Günnemann
2022	J	`jnl`	Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann
2022	A*	`conf`	End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
2022	A*	`conf`	Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
2022	J	`jnl`	On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann
2022	A*	`conf`	Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
2022	J	`jnl`	Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
2021	A*	`conf`	Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
2021	A*	`conf`	Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
2021	J	`jnl`	Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
2021	J	`jnl`	Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
2021	J	`jnl`	On Out-of-distribution Detection with Energy-based Models. CoRR Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann
2020	A*	`conf`	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
2020	J	`jnl`	Scikit-network: Graph Analysis in Python. CoRR Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier
2020	J	`jnl`	Scikit-network: Graph Analysis in Python. J. Mach. Learn. Res. Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier
2019	A*	`conf`	Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering. IJCAI Bertrand Charpentier, Thomas Bonald
2019	A*	`conf`	Uncertainty on Asynchronous Time Event Prediction. NeurIPS Bertrand Charpentier, Marin Bilos, Stephan Günnemann
2019	J	`jnl`	Uncertainty on Asynchronous Time Event Prediction. CoRR Marin Bilos, Bertrand Charpentier, Stephan Günnemann
2018	J	`jnl`	Hierarchical Graph Clustering using Node Pair Sampling. CoRR Thomas Bonald, Bertrand Charpentier, Alexis Galland, Alexandre Hollocou
2018	J	`jnl`	Learning Graph Representations by Dendrograms. CoRR Thomas Bonald, Bertrand Charpentier