Loading...
The system can't perform the operation now. Try again later.
Citations per year
Duplicate citations
The following articles are merged in Scholar. Their
combined citations
are counted only for the first article.
Merged citations
This "Cited by" count includes citations to the following articles in Scholar. The ones marked
*
may be different from the article in the profile.
Add co-authors
Co-authors
Follow
New articles by this author
New citations to this author
New articles related to this author's research
Email address for updates
Done
My profile
My library
Metrics
Alerts
Settings
Sign in
Sign in
Get my own profile
Cited by
All
Since 2019
Citations
695
694
h-index
3
3
i10-index
3
3
0
300
150
75
225
2020
2021
2022
2023
2024
3
97
171
282
138
Public access
View all
View all
2 articles
0 articles
available
not available
Based on funding mandates
Co-authors
Mingrui Liu
George Mason University
Verified email at gmu.edu
Yajie Bao
Shanghai Jiao Tong University
Verified email at sjtu.edu.cn
zhenxun zhuang
Meta
Verified email at fb.com
Francesco Orabona
Associate Professor, KAUST
Verified email at orabona.com
Wei Zhang
IBM T.J.Watson Research Center
Verified email at us.ibm.com
Follow
Michael Crawshaw
George Mason University
Verified email at gmu.edu -
Homepage
Machine learning
optimization
deep learning
federated learning
Articles
Cited by
Public access
Co-authors
Title
Sort
Sort by citations
Sort by year
Sort by title
Cited by
Cited by
Year
Multi-task learning with deep neural networks: A survey
M Crawshaw
arXiv preprint arXiv:2009.09796
, 2020
647
2020
Robustness to unbounded smoothness of generalized signsgd
M Crawshaw, M Liu, F Orabona, W Zhang, Z Zhuang
Advances in Neural Information Processing Systems 35, 9955-9968
, 2022
33
2022
Fast Composite Optimization and Statistical Recovery in Federated Learning
Y Bao, M Crawshaw, S Luo, M Liu
International Conference on Machine Learning, 1508-1536
, 2022
10
2022
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
M Crawshaw, Y Bao, M Liu
International Conference on Learning Representations
, 2023
3
2023
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
M Crawshaw, Y Bao, M Liu
Thirty-seventh Conference on Neural Information Processing Systems
, 2023
2
2023
The system can't perform the operation now. Try again later.
Articles 1–5
Show more
Privacy
Terms
Help
About Scholar
Search help