Hongyi Wang

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Dimitris PapailiopoulosResearcher at Microsoft Research & Assoc. Professor at University of WisconsinVerified email at papail.io
Chaoyang HeCo-founder, TensorOpera AI and FedML; PhD in CS from University of Southern CaliforniaVerified email at usc.edu
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Zachary CharlesResearch Scientist, GoogleVerified email at google.com
Yuekai SunUniversity of MichiganVerified email at umich.edu
Mikhail YurochkinResearch Manager, Statistical Large Language Modeling, IBM Research and MIT-IBM Watson AI LabVerified email at umich.edu
Kangwook LeeUniversity of Wisconsin-MadisonVerified email at wisc.edu
Lingjiao ChenStanford UniversityVerified email at stanford.edu
Shashank RajputResearch Scientist, MosaicML (Databricks)Verified email at databricks.com
Yasaman KhazaeniCapitalOne, Wayfair, IBM, Boston UniversityVerified email at bu.edu
Jy-yong SohnYonsei UniversityVerified email at yonsei.ac.kr
Kartik SreenivasanGraduate Student, University of Wisconsin-MadisonVerified email at wisc.edu
Shivaram VenkataramanUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Tianhua TaoPaul G. Allen School of Computer Science & Engineering, University of WashingtonVerified email at cs.washington.edu
Harit VishwakarmaCS PhD Candidate, University of Wisconsin MadisonVerified email at cs.wisc.edu
Songze LiProfessor, Southeast UniversityVerified email at seu.edu.cn
Stephen WrightDepartment of Computer Sciences and Wisconsin Institute for Discovery, University of WisconsinVerified email at cs.wisc.edu
Shengchao LiuUC BerkeleyVerified email at berkeley.edu
Dacheng LiUC Berkeley; NVIDIAVerified email at berkeley.edu
Hao ZhangUC San DiegoVerified email at ucsd.edu

Hongyi Wang

GenBio AI; Incoming Assistant Professor, Rutgers University

Verified email at genbio.ai - Homepage

ML Systems


Title Sort by citations Sort by year Sort by 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	1367	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	703	2020
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	673*	2020
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	420	2018
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	409	2021
Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018	307*	2018
Position: TrustLLM: Trustworthiness in large language models Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ... International Conference on Machine Learning, 20166-20270, 2024	278*	2024
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	141	2019
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	74	2022
Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019	65	2019
Pufferfish: Communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021	57	2021
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	54	2023
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	52	2022
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	47*	2021
Rare Gems: Finding Lottery Tickets at Initialization K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ... NeurIPS 2022, 2022	43	2022
Slimpajama-dc: Understanding data combinations for llm training Z Shen, T Tao, L Ma, W Neiswanger, Z Liu, H Wang, B Tan, J Hestness, ... arXiv preprint arXiv:2309.10818, 2023	40	2023
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	39	2022
Fusing models with complementary expertise H Wang, FM Polo, Y Sun, S Kundu, E Xing, M Yurochkin arXiv preprint arXiv:2310.01542, 2023	26	2023
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	24	2018
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness D Li, H Wang, E Xing, H Zhang NeurIPS 2022, 2022	20	2022

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