1. Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention

   
   Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot
   In Proceedings of the International Conference on Machine Learning (ICML) (2025) Conference
   

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2. I Know What I Don’t Know: Improving Model Cascades Through Confidence Tuning

   
   Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar, Wittawat Jitkrittum, Sean Augenstein, Congchao Wang, Federico Tombari
   arXiv preprint arXiv:2502.19335 (2025) Preprint
   

   Paper

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3. Selective Prediction Via Training Dynamics

   
   Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
   In Transactions on Machine Learning Research (2025) Journal
   

   Paper Slides

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4. Suitability Filter: A Statistical Framework for Model Evaluation in Real-World Deployment Settings

   
   Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot
   In Proceedings of the International Conference on Machine Learning (ICML) (2025) Conference Spotlight
   

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5. Robust and Actively Secure Serverless Collaborative Learning

   
   Olive Franzese*, Adam Dziedzic*, Christopher A Choquette-Choo, Mark R Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
   Advances in Neural Information Processing Systems (2023) Conference
   

   Paper

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6. Training Private Models That Know What They Don’t Know

   
   Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
   In Advances in Neural Information Processing Systems (2023) Conference
   

   Paper Slides

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7. 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
   

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8. 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
   

   Paper

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9. 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

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10. 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

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11. Denoising Spectral Clustering Through Latent Data Decomposition

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

    Paper

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12. Improving Online GMM Learning Via Covariance Weighting

    
    Stephan Rabanser, Maksim Greiner
    (2018) Preprint
    

    Paper

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

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

    Paper

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* indicates joint first-authorship.