Hongyi Wang

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Dimitris PapailiopoulosAssociate Professor, University of Wisconsin-MadisonVerified email at papail.io
Zachary CharlesResearch Scientist, GoogleVerified email at google.com
Chaoyang HeCo-founder & CTO at FedML Inc; PhD in CS from University of Southern CaliforniaVerified email at usc.edu
Yasaman KhazaeniCapitalOne, Wayfair, IBM, Boston UniversityVerified email at bu.edu
Mikhail YurochkinResearch Staff Member, IBM Research and MIT-IBM Watson AI LabVerified email at umich.edu
Kangwook LeeUniversity of Wisconsin-MadisonVerified email at wisc.edu
Shashank RajputGraduate Student, University of Wisconsin - MadisonVerified email at wisc.edu
Jy-yong SohnYonsei UniversityVerified email at yonsei.ac.kr
Kartik SreenivasanGraduate Student, University of Wisconsin-MadisonVerified email at wisc.edu
Yuekai SunAssistant Professor of Statistics, University of MichiganVerified email at umich.edu
Stephen WrightDepartment of Computer Sciences and Wisconsin Institute for Discovery, University of WisconsinVerified email at cs.wisc.edu
Shengchao LiuMila-UdeMVerified email at umontreal.ca
Harit VishwakarmaGraduate Student, University of Wisconsin MadisonVerified email at cs.wisc.edu
Songze LiAssistant Professor, Hong Kong University of Science and TechnologyVerified email at ust.hk
Shivaram VenkataramanUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Paraschos KoutrisUniversity of WashingtonVerified email at cs.washington.edu
Jinman ZhaoAmazon Web ServicesVerified email at amazon.com
Dacheng LiCarnegie Mellon UniversityVerified email at cs.cmu.edu
Han GuoCarnegie Mellon UniversityVerified email at cs.cmu.edu

Hongyi Wang

Senior Researcher, Carnegie Mellon University

Verified email at andrew.cmu.edu - Homepage

ML Systems


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Federated Learning with Matched Averaging H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni ICLR 2020 - International Conference on Learning Representations, 2020	634	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	297	2018
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	295*	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	287	2020
Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018	237*	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	180	2021
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	90	2019
Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019	44	2019
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	22	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	21	2021
Pufferfish: Communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021	19	2021
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	18	2018
Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation K Zhang, Y Wang, H Wang, L Huang, C Yang, L Sun arXiv preprint arXiv:2203.09553, 2022	8	2022
Rare Gems: Finding Lottery Tickets at Initialization K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ... NeurIPS 2022, 2022	6	2022
Recognizing actions during tactile manipulations through force sensing G Subramani, D Rakita, H Wang, J Black, M Zinn, M Gleicher 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017	5	2017
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	2	2023
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	2	2022
Avoiding negative transfer on a focused task with deep multi-task reinforcement learning YLAGS Liu, H Wang, Y Liang, A Gitter	2	2017
Cuttlefish: Low-rank Model Training without All The Tuning H Wang, S Agarwal, Y Tanaka, EP Xing, D Papailiopoulos arXiv preprint arXiv:2305.02538, 2023		2023
Memory-adaptive Depth-wise Heterogenous Federated Learning K Zhang, Y Dai, H Wang, E Xing, X Chen, L Sun arXiv preprint arXiv:2303.04887, 2023		2023

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