Introduction to Tensor Decompositions and their Applications in Machine Learning. - Stephan Rabanser; Oleksandr Shchur; Stephan Günnemann (CoRR, 2017)

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. - Stephan Rabanser; Stephan Günnemann; Zachary C. Lipton (NeurIPS, 2019)

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. - Stephan Rabanser; Tim Januschowski; Valentin Flunkert; David Salinas; Jan Gasthaus (CoRR, 2020)

Selective Classification Via Neural Network Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Nicolas Papernot (CoRR, 2022)

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 (CoRR, 2022)

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 (CoRR, 2022)

Robust and Actively Secure Serverless Collaborative Learning. - Nicholas Franzese; Adam Dziedzic; Christopher A. Choquette-Choo; Mark R. Thomas; Muhammad Ahmad Kaleem; Stephan Rabanser; Congyu Fang; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (NeurIPS, 2023)

Training Private Models That Know What They Don't Know. - Stephan Rabanser; Anvith Thudi; Abhradeep Guha Thakurta; Krishnamurthy Dvijotham; Nicolas Papernot (NeurIPS, 2023)

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 012 (CoRR, 2023)

Selective Prediction via Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Israfil Bahceci; Akram Bin Sediq; Hamza Umit Sokun; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

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 (CoRR, 2025)

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. - Angéline Pouget; Mohammad Yaghini; Stephan Rabanser; Nicolas Papernot (CoRR, 2025)

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. - Stephan Rabanser; Ali Shahin Shamsabadi; Olive Franzese; Xiao Wang 012; Adrian Weller; Nicolas Papernot (CoRR, 2025)

Cascadia: A Cascade Serving System for Large Language Models. - Youhe Jiang; Fangcheng Fu; Wanru Zhao; Stephan Rabanser; Nicholas D. Lane; Binhang Yuan (CoRR, 2025)