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Rajiv Mathews
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2020 – today
- 2024
- [c16]Lun Wang, Om Thakkar, Rajiv Mathews:
Unintended Memorization in Large ASR Models, and How to Mitigate It. ICASSP 2024: 4655-4659 - [c15]Renkun Ni, Yonghui Xiao, Phoenix Meadowlark, Oleg Rybakov, Tom Goldstein, Ananda Theertha Suresh, Ignacio López-Moreno, Mingqing Chen, Rajiv Mathews:
FedAQT: Accurate Quantized Training with Federated Learning. ICASSP 2024: 6100-6104 - [i29]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) - 2023
- [c14]Lillian Zhou, Yuxin Ding, Mingqing Chen, Harry Zhang, Rohit Prabhavalkar, Dhruv Guliani, Giovanni Motta, Rajiv Mathews:
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections Through Federated Learning. ASRU 2023: 1-7 - [c13]Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv Mathews, Mingqing Chen:
Online Model Compression for Federated Learning with Large Models. ICASSP 2023: 1-5 - [i28]Lillian Zhou, Yuxin Ding, Mingqing Chen, Harry Zhang, Rohit Prabhavalkar, Dhruv Guliani, Giovanni Motta, Rajiv Mathews:
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning. CoRR abs/2310.00141 (2023) - [i27]Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang, Rohan Anil, Rajiv Mathews:
Heterogeneous Federated Learning Using Knowledge Codistillation. CoRR abs/2310.02549 (2023) - [i26]Lun Wang, Om Thakkar, Rajiv Mathews:
Unintended Memorization in Large ASR Models, and How to Mitigate It. CoRR abs/2310.11739 (2023) - 2022
- [c12]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. ICASSP 2022: 4338-4342 - [c11]Hao Zhang, You-Chi Cheng, Shankar Kumar, W. Ronny Huang, Mingqing Chen, Rajiv Mathews:
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model. ICASSP 2022: 6097-6101 - [c10]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. ICML 2022: 517-535 - [c9]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 - [c8]Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg, Rajiv Mathews, Khe Chai Sim, Kilol Gupta, Mingqing Chen, Lara McConnaughey:
UserLibri: A Dataset for ASR Personalization Using Only Text. INTERSPEECH 2022: 694-698 - [c7]Ehsan Amid, Om Dipakbhai Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. INTERSPEECH 2022: 2803-2807 - [c6]W. Ronny Huang, Steve Chien, Om Dipakbhai Thakkar, Rajiv Mathews:
Detecting Unintended Memorization in Language-Model-Fused ASR. INTERSPEECH 2022: 2808-2812 - [i25]Hao Zhang, You-Chi Cheng, Shankar Kumar, W. Ronny Huang, Mingqing Chen, Rajiv Mathews:
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model. CoRR abs/2202.08171 (2022) - [i24]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) - [i23]Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. CoRR abs/2204.08345 (2022) - [i22]W. Ronny Huang, Steve Chien, Om Thakkar, Rajiv Mathews:
Detecting Unintended Memorization in Language-Model-Fused ASR. CoRR abs/2204.09606 (2022) - [i21]Jae Hun Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Theertha Suresh, Shankar Kumar, Rajiv Mathews:
Scaling Language Model Size in Cross-Device Federated Learning. CoRR abs/2204.09715 (2022) - [i20]Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv Mathews, Mingqing Chen:
Online Model Compression for Federated Learning with Large Models. CoRR abs/2205.03494 (2022) - [i19]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) - [i18]Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg, Rajiv Mathews, Khe Chai Sim, Kilol Gupta, Mingqing Chen, Lara McConnaughey:
UserLibri: A Dataset for ASR Personalization Using Only Text. CoRR abs/2207.00706 (2022) - [i17]Sandy Ritchie, You-Chi Cheng, Mingqing Chen, Rajiv Mathews, Daan van Esch, Bo Li, Khe Chai Sim:
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning. CoRR abs/2208.03067 (2022) - [i16]Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta:
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR abs/2210.01864 (2022) - 2021
- [c5]Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh:
Communication-Efficient Agnostic Federated Averaging. Interspeech 2021: 871-875 - [c4]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. NeurIPS 2021: 1727-1738 - [i15]Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh:
Communication-Efficient Agnostic Federated Averaging. CoRR abs/2104.02748 (2021) - [i14]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It. CoRR abs/2104.07815 (2021) - [i13]Hao Zhang, You-Chi Cheng, Shankar Kumar, Mingqing Chen, Rajiv Mathews:
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network. CoRR abs/2108.11943 (2021) - [i12]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. CoRR abs/2111.00556 (2021) - [i11]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) - [i10]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. CoRR abs/2112.00193 (2021) - 2020
- [c3]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 - [c2]Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio López-Moreno, Rajiv Mathews:
Training Keyword Spotting Models on Non-IID Data with Federated Learning. INTERSPEECH 2020: 4343-4347 - [i9]Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio López-Moreno, Rajiv Mathews:
Training Keyword Spotting Models on Non-IID Data with Federated Learning. CoRR abs/2005.10406 (2020) - [i8]Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Françoise Beaufays:
Understanding Unintended Memorization in Federated Learning. CoRR abs/2006.07490 (2020) - [i7]Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays:
Training Production Language Models without Memorizing User Data. CoRR abs/2009.10031 (2020)
2010 – 2019
- 2019
- [c1]Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, Michael Riley:
Federated Learning of N-Gram Language Models. CoNLL 2019: 121-130 - [i6]Mingqing Chen, Rajiv Mathews, Tom Ouyang, Françoise Beaufays:
Federated Learning Of Out-Of-Vocabulary Words. CoRR abs/1903.10635 (2019) - [i5]Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, Françoise Beaufays:
Federated Learning for Emoji Prediction in a Mobile Keyboard. CoRR abs/1906.04329 (2019) - [i4]Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, Michael Riley:
Federated Learning of N-gram Language Models. CoRR abs/1910.03432 (2019) - [i3]Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage:
Federated Evaluation of On-device Personalization. CoRR abs/1910.10252 (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)
Coauthor Index
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