Sean Augenstein

16 papers A* 4A 1Journal 9Unranked 2

Year	Rank	Type	Title / Venue / Authors
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
2024	J	`jnl`	Cascade-Aware Training of Language Models. CoRR Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go
2024	J	`jnl`	Learning from straggler clients in federated learning. CoRR Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil
2023	A*	`conf`	Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2022	J	`jnl`	Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2022	J	`jnl`	Mixed Federated Learning: Joint Decentralized and Centralized Learning. CoRR Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews
2022	A	`conf`	Production federated keyword spotting via distillation, filtering, and joint federated-centralized training. INTERSPEECH Andrew Hard, Kurt Partridge, Neng Chen, Sean Augenstein, Aishanee Shah, Hyun Jin Park, Alex Park, Sara Ng, Jessica Nguyen, Ignacio López-Moreno, Rajiv Mathews, Françoise Beaufays
2022	J	`jnl`	Production federated keyword spotting via distillation, filtering, and joint federated-centralized training. CoRR Andrew Hard, Kurt Partridge, Neng Chen, Sean Augenstein, Aishanee Shah, Hyun Jin Park, Alex Park, Sara Ng, Jessica Nguyen, Ignacio López-Moreno, Rajiv Mathews, Françoise Beaufays
2021	J	`jnl`	Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift. CoRR Sean Augenstein, Andrew Hard, Kurt Partridge, Rajiv Mathews
2020	A*	`conf`	Generative Models for Effective ML on Private, Decentralized Datasets. ICLR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2019	J	`jnl`	Generative Models for Effective ML on Private, Decentralized Datasets. CoRR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2018	J	`jnl`	Federated Learning for Mobile Keyboard Prediction. CoRR Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage
2016	A*	`conf`	Optimal Scheduling of a Constellation of Earth-Imaging Satellites, for Maximal Data Throughput and Efficient Human Management. ICAPS Sean Augenstein, Alejandra Estanislao, Emmanuel Guere, Sean Blaes
2014	—	`conf`	Optimal Scheduling of Earth-Imaging Satellites with Human Collaboration via Directed Acyclic Graphs. AAAI Spring Symposia Sean Augenstein
2012	—	`conf`	Tumbling target reconstruction and pose estimation through fusion of monocular vision and sparse-pattern range data. MFI Jose Padial, Marcus Hammond, Sean Augenstein, Stephen M. Rock
2011	A*	`conf`	Improved frame-to-frame pose tracking during vision-only SLAM/SFM with a tumbling target. ICRA Sean Augenstein, Stephen M. Rock