Bertrand Charpentier

发表

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions pdf

Stephan Gunnemann, Bertrand Charpentier, Simon Geisler, 2022, ICLR.

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Stephan Günnemann, Daniel Zügner, Anna-Kathrin Kopetzki, 2020, ICML.

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning

Mykel J. Kochenderfer, Stephan Günnemann, Ransalu Senanayake, 2022, ArXiv.

Hierarchical Graph Clustering by Node Pair Sampling

T. Bonald, Alexandre Hollocou, Bertrand Charpentier, 2018 .

E ND - TO -E ND L EARNING OF P ROBABILISTIC H IERARCHIES ON G RAPHS

Bertrand Charpentier, 2022 .

Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions

Bertrand Charpentier, Oliver Borchert, D. Zugner, 2021, 2105.04471.

On the Robustness and Anomaly Detection of Sparse Neural Networks

Bertrand Charpentier, D. Zugner, Simon Geisler, 2022, ArXiv.

Winning the Lottery Ahead of Time: Efficient Early Network Pruning

Bertrand Charpentier, D. Zugner, Simon Geisler, 2022, ICML.

Scikit-network: Graph Analysis in Python

Thomas Bonald, Bertrand Charpentier, Nathan de Lara, 2020, J. Mach. Learn. Res..

Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering

Thomas Bonald, Bertrand Charpentier, T. Bonald, 2019, IJCAI.

Learning Graph Representations by Dendrograms pdf

Thomas Bonald, Bertrand Charpentier, T. Bonald, 2018, ArXiv.

Hierarchical Graph Clustering using Node Pair Sampling pdf

Alexandre Hollocou, Thomas Bonald, Bertrand Charpentier, 2018, ArXiv.

Edge Directionality Improves Learning on Heterophilic Graphs

Stephan Günnemann, Francesco Di Giovanni, Stephan Günnemann, 2023, ArXiv.

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification pdf

Stephan Gunnemann, Bertrand Charpentier, Simon Geisler, 2021, NeurIPS.

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Stephan Günnemann, Bertrand Charpentier, Daniel Zügner, 2020, NeurIPS.

Uncertainty on Asynchronous Time Event Prediction

Stephan Günnemann, Bertrand Charpentier, Marin Bilos, 2019, NeurIPS.

Differentiable DAG Sampling

Stephan Günnemann, Bertrand Charpentier, Simon Kibler, 2022, ICLR.

Uncertainty Estimation for Molecules: Desiderata and Methods

Bertrand Charpentier, Stephan Gunnemann, Nicholas Gao, 2023, ICML.

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Stephan Günnemann, Anna-Kathrin Kopetzki, Bertrand Charpentier, 2020, ICML.

Training, Architecture, and Prior for Deterministic Uncertainty Methods

Stephan Günnemann, Chenxiang Zhang, Bertrand Charpentier, 2023, ArXiv.

Edge Directionality Improves Learning on Heterophilic Graphs

Francesco Di Giovanni, Stephan Günnemann, Bertrand Charpentier, 2023, LoG.

Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models

Stephan Günnemann, Bertrand Charpentier, Johannes Getzner, 2023, ArXiv.

End-to-End Learning of Probabilistic Hierarchies on Graphs

Stephan Günnemann, Bertrand Charpentier, Daniel Zügner, 2022, ICLR.

Uncertainty for Active Learning on Graphs

Bertrand Charpentier, Stephan Günnemann, Dominik Fuchsgruber, 2024, ArXiv.