20 citations · 52 across the 5 of their papers we have counts for
8 papers
Differentiable DAG Sampling
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG sa…
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler, Bertrand Charpentier, Simon Geisler +2
The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). N…
On Out-of-distribution Detection with Energy-based Models
Sven Elflein, Bertrand Charpentier, Daniel Zügner +1
Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data. Energy-based models (EBMs) are…
Scikit-network: Graph Analysis in Python
Thomas Bonald, Nathan de Lara, Quentin Lutz +1
Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. Th…
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate unc…
Uncertainty on Asynchronous Time Event Prediction
Marin Biloš, Bertrand Charpentier, Stephan Günnemann
Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), an…