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Sean Augenstein

3 accepted papers

2025

Gatekeeper: Improving Model Cascades Through Confidence Tuning

Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar, Wittawat Jitkrittum, Sean Augenstein +2
NeurIPS 2025poster

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 challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to o…

Cited by 0Source ↗Scholar ↗
2023

Learning To Generate Image Embeddings With User-Level Differential Privacy

Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein +3
CVPR 2023poster

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class sp…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2020

Generative Models for Effective ML on Private, Decentralized Datasets

Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen +2
ICLR 2020poster

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data—of representative samples, of outliers, of misclassifications—is an essential tool in a) identifying and fixing p…

Cited by 232Source ↗Scholar ↗