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Hongyi Wang
Hongyi Wang
Senior Project Scientist, Carnegie Mellon University
Verified email at andrew.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Federated Learning with Matched Averaging
H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni
ICLR 2020 - International Conference on Learning Representations, 2020
10572020
Fedml: A research library and benchmark for federated machine learning
C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ...
arXiv preprint arXiv:2007.13518, 2020
521*2020
Attack of the tails: Yes, you really can backdoor federated learning
H Wang, K Sreenivasan, S Rajput, H Vishwakarma, S Agarwal, J Sohn, ...
Advances in Neural Information Processing Systems 33, 16070-16084, 2020
5132020
Atomo: Communication-efficient learning via atomic sparsification
H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright
Advances in neural information processing systems 31, 2018
3622018
A field guide to federated optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
3282021
Draco: Byzantine-resilient distributed training via redundant gradients
L Chen, H Wang, Z Charles, D Papailiopoulos
International Conference on Machine Learning, 903-912, 2018
281*2018
DETOX: A redundancy-based framework for faster and more robust gradient aggregation
S Rajput, H Wang, Z Charles, D Papailiopoulos
Advances in Neural Information Processing Systems 32, 2019
1222019
Trustllm: Trustworthiness in large language models
L Sun, Y Huang, H Wang, S Wu, Q Zhang, C Gao, Y Huang, W Lyu, ...
arXiv preprint arXiv:2401.05561, 2024
592024
Erasurehead: Distributed gradient descent without delays using approximate gradient coding
H Wang, Z Charles, D Papailiopoulos
arXiv preprint arXiv:1901.09671, 2019
572019
On the utility of gradient compression in distributed training systems
S Agarwal, H Wang, S Venkataraman, D Papailiopoulos
Proceedings of Machine Learning and Systems 4, 652-672, 2022
382022
Pufferfish: Communication-efficient models at no extra cost
H Wang, S Agarwal, D Papailiopoulos
Proceedings of Machine Learning and Systems 3, 365-386, 2021
382021
Adaptive gradient communication via critical learning regime identification
S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos
Proceedings of Machine Learning and Systems 3, 55-80, 2021
35*2021
MPCFormer: fast, performant and private Transformer inference with MPC
D Li, R Shao, H Wang, H Guo, EP Xing, H Zhang
arXiv preprint arXiv:2211.01452, 2022
332022
Rare Gems: Finding Lottery Tickets at Initialization
K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ...
NeurIPS 2022, 2022
282022
The effect of network width on the performance of large-batch training
L Chen, H Wang, J Zhao, D Papailiopoulos, P Koutris
Advances in neural information processing systems 31, 2018
242018
Llm360: Towards fully transparent open-source llms
Z Liu, A Qiao, W Neiswanger, H Wang, B Tan, T Tao, J Li, Y Wang, S Sun, ...
arXiv preprint arXiv:2312.06550, 2023
172023
Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation
K Zhang, Y Wang, H Wang, L Huang, C Yang, X Chen, L Sun
arXiv preprint arXiv:2203.09553, 2022
152022
Slimpajama-dc: Understanding data combinations for llm training
Z Shen, T Tao, L Ma, W Neiswanger, J Hestness, N Vassilieva, ...
arXiv preprint arXiv:2309.10818, 2023
112023
Federated learning as variational inference: A scalable expectation propagation approach
H Guo, P Greengard, H Wang, A Gelman, Y Kim, EP Xing
arXiv preprint arXiv:2302.04228, 2023
72023
Demonstration of nimbus: Model-based pricing for machine learning in a data marketplace
L Chen, H Wang, L Chen, P Koutris, A Kumar
Proceedings of the 2019 International Conference on Management of Data, 1885 …, 2019
62019
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