5 citations · 9 across the 11 of their papers we have counts for
6 papers · 1 filter
What Does It Take to Build a Performant Selective Classifier?
Stephan Rabanser, Nicolas Papernot
Selective classifiers improve model reliability by abstaining on inputs the model deems uncertain. However, few practical approaches achieve the gold-standard performance of a perf…
Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning
Stephan Rabanser
Machine learning (ML) systems are increasingly deployed in high-stakes domains where reliability is paramount. This thesis investigates how uncertainty estimation can enhance the s…
Cascadia: An Efficient Cascade Serving System for Large Language Models
Youhe Jiang, Fangcheng Fu, Wanru Zhao +4
Recent advances in large language models (LLMs) have intensified the need to deliver both rapid responses and high-quality outputs. More powerful models yield better results but in…
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese +3
Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify…
Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings
Angéline Pouget, Mohammad Yaghini, Stephan Rabanser +1
Deploying machine learning models in safety-critical domains poses a key challenge: ensuring reliable model performance on downstream user data without access to ground truth label…
Gatekeeper: Improving Model Cascades Through Confidence Tuning
Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha +5
Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these chal…