1. Selective Classification Via Neural Network Training Dynamics

    Stephan Rabanser*, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
    arXiv preprint arXiv:2205.13532 (2022) Preprint

    Paper Slides

  2. Intrinsic Anomaly Detection for Multi-Variate Time Series

    Stephan Rabanser*, Tim Januschowski*, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
    arXiv preprint arXiv:2206.14342 (2022) Preprint


  3. p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations

    Adam Dziedzic*, Stephan Rabanser*, Mohammad Yaghini*, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
    arXiv preprint arXiv:2207.12545 (2022) Preprint


  4. The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models

    Stephan Rabanser*, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
    In 7th KDD Workshop on Mining and Learning from Time Series (MiLeTS) (2020) Workshop Oral

    Paper Slides Video

  5. Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

    Stephan Rabanser*, Stephan Günnemann, Zachary Lipton
    In Advances in Neural Information Processing Systems (2019) Conference

    Paper Poster Slides Code

  6. Improving Online GMM Learning Via Covariance Weighting

    Stephan Rabanser*, Maksim Greiner
    (2018) Preprint


  7. Denoising Spectral Clustering Through Latent Data Decomposition

    Stephan Rabanser*, Oleksandr Shchur, Stephan Günnemann
    (2018) Preprint


  8. Introduction to Tensor Decompositions and their Applications in Machine Learning

    Stephan Rabanser*, Oleksandr Shchur, Stephan Günnemann
    arXiv preprint arXiv:1711.10781 (2017) Preprint


* indicates joint first-authorship.