Stephan Rabanser

21 papers A* 5Journal 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Towards a Science of AI Agent Reliability. CoRR Stephan Rabanser, Sayash Kapoor, Peter Kirgis, Kangheng Liu, Saiteja Utpala, Arvind Narayanan
2025	J	`jnl`	Cascadia: A Cascade Serving System for Large Language Models. CoRR Youhe Jiang, Fangcheng Fu, Wanru Zhao, Stephan Rabanser, Nicholas D. Lane, Binhang Yuan
2025	A*	`conf`	Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. ICML Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot
2025	J	`jnl`	Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. CoRR Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang, Adrian Weller, Nicolas Papernot
2025	J	`jnl`	I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning. CoRR Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar, Wittawat Jitkrittum, Sean Augenstein, Congchao Wang, Federico Tombari
2025	J	`jnl`	Selective Prediction via Training Dynamics. Trans. Mach. Learn. Res. Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot
2025	A*	`conf`	Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. ICML Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot
2025	J	`jnl`	Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. CoRR Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot
2025	J	`jnl`	Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning. CoRR Stephan Rabanser
2025	J	`jnl`	What Does It Take to Build a Performant Selective Classifier? CoRR Stephan Rabanser, Nicolas Papernot
2023	A*	`conf`	Robust and Actively Secure Serverless Collaborative Learning. NeurIPS Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	J	`jnl`	Robust and Actively Secure Serverless Collaborative Learning. CoRR Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	A*	`conf`	Training Private Models That Know What They Don't Know. NeurIPS Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
2023	J	`jnl`	Training Private Models That Know What They Don't Know. CoRR Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
2022	J	`jnl`	Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
2022	J	`jnl`	Selective Classification Via Neural Network Training Dynamics. CoRR Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
2022	J	`jnl`	p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
2020	J	`jnl`	The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. CoRR Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
2019	A*	`conf`	Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. NeurIPS Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
2018	J	`jnl`	Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. CoRR Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
2017	J	`jnl`	Introduction to Tensor Decompositions and their Applications in Machine Learning. CoRR Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann