Four papers (incl. one oral) accepted at NeurIPS 2020


Our group has four papers accepted at NeurIPS 2020, including one oral presentation (top 1% of submitted works)!
The works cover the full range of our core research topics: machine learning for graphs, machine learning for temporal data, and reliability of ML methods, i.e. robustness & uncertainty. 

  • Fast and Flexible Temporal Point Processes with Triangular Maps (oral)
    Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann
    preprint

  • Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
    Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
    preprint

  • Reliable Graph Neural Networks via Robust Location Estimation
    Simon Geisler, Daniel Zügner, Stephan Günnemann

  • Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
    Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
    joint work with Amazon Research

Congratulations to all co-authors!