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2020 – today
- 2024
[i8]Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil:
Learning from straggler clients in federated learning. CoRR abs/2403.09086 (2024)- [i7]Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go:
Cascade-Aware Training of Language Models. CoRR abs/2406.00060 (2024) - 2023
- [c7]Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan:
Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR 2023: 7969-7980 - 2022
- [c6]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:
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training. INTERSPEECH 2022: 76-80 - [i6]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:
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training. CoRR abs/2204.06322 (2022) - [i5]Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews:
Mixed Federated Learning: Joint Decentralized and Centralized Learning. CoRR abs/2205.13655 (2022) - [i4]Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan:
Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR abs/2211.10844 (2022) - 2021
- [i3]Sean Augenstein, Andrew Hard, Kurt Partridge, Rajiv Mathews:
Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift. CoRR abs/2111.12150 (2021) - 2020
- [c5]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. ICLR 2020
2010 – 2019
- 2019
- [i2]Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas:
Generative Models for Effective ML on Private, Decentralized Datasets. CoRR abs/1911.06679 (2019) - 2018
- [i1]Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage:
Federated Learning for Mobile Keyboard Prediction. CoRR abs/1811.03604 (2018) - 2016
- [c4]Sean Augenstein, Alejandra Estanislao, Emmanuel Guere, Sean Blaes:
Optimal Scheduling of a Constellation of Earth-Imaging Satellites, for Maximal Data Throughput and Efficient Human Management. ICAPS 2016: 345-352 - 2014
- [c3]Sean Augenstein:
Optimal Scheduling of Earth-Imaging Satellites with Human Collaboration via Directed Acyclic Graphs. AAAI Spring Symposia 2014 - 2012
- [c2]Jose Padial, Marcus Hammond, Sean Augenstein, Stephen M. Rock:
Tumbling target reconstruction and pose estimation through fusion of monocular vision and sparse-pattern range data. MFI 2012: 419-425 - 2011
- [c1]Sean Augenstein, Stephen M. Rock:
Improved frame-to-frame pose tracking during vision-only SLAM/SFM with a tumbling target. ICRA 2011: 3131-3138
Coauthor Index
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