Stephan Rabanser

According to our database, Stephan Rabanser authored at least 17 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Towards a Science of AI Agent Reliability.

Stephan Rabanser, Sayash Kapoor, Peter Kirgis, Kangheng Liu, Saiteja Utpala, Arvind Narayanan

CoRR, February, 2026

2025

What Does It Take to Build a Performant Selective Classifier?

Stephan Rabanser, Nicolas Papernot

CoRR, October, 2025

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning.

Stephan Rabanser

CoRR, August, 2025

Cascadia: A Cascade Serving System for Large Language Models.

Youhe Jiang, Fangcheng Fu, Wanru Zhao, Stephan Rabanser, Nicholas D. Lane, Binhang Yuan

CoRR, June, 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, February, 2025

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

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention.

Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot

Proceedings of the Forty-second International Conference on Machine Learning, 2025

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

Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot

Proceedings of the Forty-second International Conference on Machine Learning, 2025

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

CoRR, 2023

Training Private Models That Know What They Don't Know.

Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

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

Selective Classification Via Neural Network Training Dynamics.

Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot

CoRR, 2022

2020

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

2019

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

Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Introduction to Tensor Decompositions and their Applications in Machine Learning.

Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann

CoRR, 2017