Bertrand Charpentier

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

Room: 02.11.037
E-Mail: charpent [at] in.tum.de

Research Focus

  • Uncertainty Estimation in Deep Learning
  • Causal Inference
  • Hierarchical and Multi-scale Clustering
  • Machine Learning for Graphs and Sequences

Publications

  • 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), 2022Spotlight 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.
    [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]