Bertrand Charpentier

Technical University of Munich
Department of Informatics - I26
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 00.11.062
E-Mail: charpent [at] in.tum.de

Website: sharpenb.github.io

Research Focus

• Uncertainty Estimation in Deep Learning
• Causality and Hierarchy in Machine Learning
• Efficient Machine Learning
• Machine Learning for Graphs and Sequential Data

Publications

• Emanuele Rossi, Bertrand Charpentier, Francesco di Giovianni, Stephan Günnemann, Michael Bronstein 
  Edge Directionality Improves Learning on Heterophilic Graphs
  Mining and Learning on Graphs (MLG - ECML PKDD), 2023.
  [Paper|Github|Publisher|Video]
• Tom Wollschlager, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Gunnemann
  Uncertainty Estimation for Molecules: Desiderata and Methods
  International Conference on Machine Learning (ICML), 2023.
  [Paper|Github|Publisher|Video]
• Joahnnes Getzner, Bertrand Charpentier, Stephan Günnemann 
  Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
  Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges Workshop (TCCML - ICLR), 2023. Spotlight talk.
  [Paper|Github|Publisher|Video]
• Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann 
  Training, Architecture, and Prior for Deterministic Uncertainty Methods
  Pitfalls of limited data and computation for Trustworthy ML Workshop (TrustML - ICLR), 2023
  [Paper|Github|Publisher|Video]
• Bertrand Charpentier, Ransalu Senanayake, Mykel Kochenderfer, Stephan Günnemann 
  Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
  Distribution-Free Uncertainty Quantification Workshop (DFUQ - ICML), 2022
  [Paper|Github|Publisher|Video]
• John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann 
  Winning the Lottery Ahead of Time: Efficient Early Network Pruning
  International Conference on Machine Learning (ICML), 2022. Spotlight talk.
  [Paper|Github|Publisher|Video]
• Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann 
  On the Robustness and Anomaly Detection of Sparse Neural Networks
  Sparsity in Neural Networks Workshop (SNN), 2022
  [Paper|Github]
• Bertrand Charpentier*, Oliver Borchert*, Daniel Zügner, Simon Geisler, Stephan Günnemann 
  Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
  International Conference on Learning Representations (ICLR), 2022. Spotlight talk.
  [Paper|Github|Publisher|Video]
• Bertrand Charpentier, Simon Kibler, Stephan Günnemann
  Differentiable DAG Sampling 
  International Conference on Learning Representations (ICLR), 2022.
  [Paper|Github|Publisher|Video]
• Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
  End-to-End Learning of Probabilistic Hierarchies on Graphs
  International Conference on Learning Representations (ICLR), 2022.
  [Paper|Github|Publisher|Video]
• Maximilian Stadler*, Bertrand Charpentier*, Simon Geisler, Daniel Zügner, Stephan Günnemann
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
  Conference on Neural Information Processing Systems (NeurIPS), 2021.
  [Paper|Github|Publisher|Video]
• Anna-Kathrin Kopetzki*, Bertrand Charpentier*, Daniel Zügner, Sandhya Giri, Stephan Günnemann
  Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
  International Conference on Machine Learning (ICML), 2021. Spotlight talk.
  [Paper|Github|Publisher]
• Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
  On Out-of-distribution Detection with Energy-Based Models
  Uncertainty and Robustness in Deep Learning Workshop (UDL - ICML), 2021.
  [Paper|Github]
• Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
  Conference on Neural Information Processing Systems (NeurIPS), 2020.
  [Paper|GitHub|Publisher|Video]
• Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier
  Scikit-network: Graph Analysis in Python
  Journal of Machine Learning Research (JMLR), 2020.
  [Paper|GitHub|Docs|Publisher]
• Marin Bilos*, Bertrand Charpentier*, Stephan Günnemann
  Uncertainty on Asynchronous Time Event Prediction
  Conference on Neural Information Processing Systems (NeurIPS), 2019. Spotlight talk.
  [Paper|GitHub|Publisher]
• Bertrand Charpentier, Thomas Bonald
  Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering
  International Joint Conferences on Artificial Intelligence (IJCAI), 2019.
  [Paper|GitHub|Publisher]
• Bertrand Charpentier
  Multi-scale Clustering in Graphs using Modularity
  KTH Publication Library (DiVA), 2019.
  [Paper|GitHub|Publisher]
• Thomas Bonald , Bertrand Charpentier, Alexis Galland, Alexandre Hollocou
  Hierarchical Graph Clustering using Node Pair Sampling
  Mining and Learning with Graphs Workshop (MLG - KDD), 2018.
  [Paper|GitHub|Publisher]

Education

• 2016 - 2018: M.Sc. in Machine Learning (passed with high distinction), KTH Royal Institute of Technology
• 2014 - 2018: M.Sc. & B.Sc. in Mathematics and Computer Science (passed with high distinction), Ensimag
• 2012 - 2014: CPGE in Mathematics and Physics, Lycee Henri IV

Software

• Scikit-Network (co-creator): Simple and efficient tools for the analysis of large graphs 
  [GitHub|Documention]