Bayesian (Deep) Learning / Uncertainty
Topics: Bayesian (Deep) Learning, Uncertainty, Probabilistic Models, (Implicit) Generative Models
Probabilistic modeling is a useful tool to analyze and understand real-world data, specifically enabling to represent the uncertainty inherent to the data and the learned model. In our research we study principle for (efficient) probabilisitc inference as well as applications of probabilistic models (often focusing on non-i.i.d. data scenarios). Specifically we also consider the combination of neural networks and Bayesian modelling -- often called "Bayesian Deep Learning" --, for example, by investigating principles for neural variational inference or deep generative models.
Selected Publications
- Dominik Fuchsgruber*, Tom Wollschläger*, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
Uncertainty for Active Learning on Graphs
International Conference on Machine Learning (ICML), 2024 - Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
Uncertainty Estimation for Molecules: Desiderata and Methods
International Conference on Machine Learning (ICML), 2023 - Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
Detecting Anomalous Event Sequences with Temporal Point Processes
Neural Information Processing Systems (NeurIPS), 2021 - 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. - Marin Bilos, Bertrand Charpentier, Stephan Günnemann
Uncertainty on Asynchronous Time Event Prediction
Conference on Neural Information Processing Systems (NeurIPS), 2019 - Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Multi-Source Neural Variational Inference
AAAI Conference on Artificial Intelligence, 2019 - Subhabrata Mukherjee and Stephan Günnemann
GhostLink: Latent Network Inference for Influence-aware Recommendation
International World Wide Web Conference (WWW / TheWebConf), 2019 - Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
NetGAN: Generating Graphs via Random Walks
International Conference on Machine Learning (ICML), 2018 - Aleksandar Bojchevski, Stephan Günnemann
Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure
AAAI Conference on Artificial Intelligence, 2018 - Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos
The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation
SIAM International Conference on Data Mining (SDM), 2017 - Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar
ZooBP: Belief Propagation for Heterogeneous Networks
International Conference on Very Large Data Bases, PVLDB 10(5): 625-636 (2017)