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ORCIDHongyi Wang 0001
Person information
- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (PhD 2021): University of Wisconsin-Madison, USA
Other persons with the same name
- Hongyi Wang (aka: Hong-Yi Wang) — disambiguation page
- Hongyi Wang 0002
![[0000-0002-2336-1496]](/https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-2336-1496")
— Zhejiang University, College of Computer Science and Technology, Hangzhou, China - Hongyi Wang 0003 — National University of Defense Technology, College of Electronic Science, Changsha, China
- Hongyi Wang 0004 — Tsinghua University, Department of Computer Science and Technology, Beijing, China
- Hongyi Wang 0005 — Tiangong University, School of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin, China (and 2 more)
- Hongyi Wang 0006 — Chongqing University of Science and Technology, School of Intelligent Technology and Engineering, Chongqing, China
- Hongyi Wang 0007 — Columbia University, New York, NY, USA
- Hongyi Wang 0008 — South China University of Technology, School of Electronics and Information Engineering, Guangzhou, China (and 1 more)
- Hongyi Wang 0009 — Tsinghua University, School of Software, Department of Electronic Engineering, Beijing, China
- Hongyi Wang 0010 (aka: Hong-Yi Wang 0010) — Xi'an Jiaotong University, School of Microelectronics, Key Laboratory of Micro-Nano Electronics and System Integration, Xi'an, China (and 2 more)
- Hongyi Wang 0011 — Aalborg University, Department of AAU Energy, Aalborg, Denmark
- Hongyi Wang 0013 — University of Maryland, Electrical Engineering Department, Institute For Systems Research, MD, USA
- Hongyi Wang 0014 — Beijing Pioneer Hi-Tech, Development Company, Beijing, China (and 1 more)
2020 – today
- 2025
[i37]Zhengzhong Liu, Bowen Tan, Hongyi Wang, Willie Neiswanger, Tianhua Tao, Haonan Li, Fajri Koto, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Liqun Ma, Liping Tang, Nikhil Ranjan, Yonghao Zhuang, Guowei He, Renxi Wang, Mingkai Deng, Robin Algayres, Yuanzhi Li, Zhiqiang Shen, Preslav Nakov, Eric P. Xing:
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch. CoRR abs/2501.07124 (2025)- [i36]Wenhao Zheng, Yixiao Chen, Weitong Zhang, Souvik Kundu, Yun Li, Zhengzhong Liu, Eric P. Xing, Hongyi Wang, Huaxiu Yao:
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing. CoRR abs/2502.01976 (2025) - 2024
- [c26]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. ICLR 2024 - [c25]Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang:
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. ICML 2024 - [c24]Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao:
Position: TrustLLM: Trustworthiness in Large Language Models. ICML 2024 - [c23]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does Compressing Activations Help Model Parallel Training? MLSys 2024 - [c22]Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu:
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs. NAACL (Demonstrations) 2024: 137-147 - [c21]Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li:
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. NeurIPS 2024 - [c20]Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, Ang Li:
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. NeurIPS 2024 - [c19]Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen:
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. NeurIPS 2024 - [i35]Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao:
TrustLLM: Trustworthiness in Large Language Models. CoRR abs/2401.05561 (2024) - [i34]Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li:
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. CoRR abs/2405.00705 (2024) - [i33]Yuan Li, Yue Huang, Hongyi Wang, Xiangliang Zhang, James Zou, Lichao Sun:
Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models. CoRR abs/2406.17675 (2024) - [i32]Zhengqing Yuan, Rong Zhou, Hongyi Wang, Lifang He, Yanfang Ye, Lichao Sun:
ViT-1.58b: Mobile Vision Transformers in the 1-bit Era. CoRR abs/2406.18051 (2024) - [i31]Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, Ang Li:
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. CoRR abs/2409.05976 (2024) - [i30]Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen:
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. CoRR abs/2410.05357 (2024) - [i29]Tianhua Tao, Junbo Li, Bowen Tan, Hongyi Wang, William Marshall, Bhargav Kanakiya, Joel Hestness, Natalia Vassilieva, Zhiqiang Shen, Eric P. Xing, Zhengzhong Liu:
Crystal: Illuminating LLM Abilities on Language and Code. CoRR abs/2411.04156 (2024) - 2023
- [c18]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR 2023 - [c17]Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang:
MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. ICLR 2023 - [c16]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. MLSys 2023 - [c15]Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang:
FedNAR: Federated Optimization with Normalized Annealing Regularization. NeurIPS 2023 - [i28]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does compressing activations help model parallel training? CoRR abs/2301.02654 (2023) - [i27]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. CoRR abs/2302.04228 (2023) - [i26]Kai Zhang, Yutong Dai, Hongyi Wang, Eric P. Xing, Xun Chen, Lichao Sun:
Memory-adaptive Depth-wise Heterogenous Federated Learning. CoRR abs/2303.04887 (2023) - [i25]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris S. Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. CoRR abs/2305.02538 (2023) - [i24]Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang:
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. CoRR abs/2308.14929 (2023) - [i23]Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing:
SlimPajama-DC: Understanding Data Combinations for LLM Training. CoRR abs/2309.10818 (2023) - [i22]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. CoRR abs/2310.01542 (2023) - [i21]Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang:
FedNAR: Federated Optimization with Normalized Annealing Regularization. CoRR abs/2310.03163 (2023) - [i20]Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu:
Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs. CoRR abs/2310.16355 (2023) - [i19]Minghao Yan, Hongyi Wang, Shivaram Venkataraman:
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices. CoRR abs/2310.19991 (2023) - [i18]Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing:
LLM360: Towards Fully Transparent Open-Source LLMs. CoRR abs/2312.06550 (2023) - 2022
- [c14]Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun:
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation. EMNLP (Findings) 2022: 613-621 - [c13]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. MLSys 2022 - [c12]Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. NeurIPS 2022 - [c11]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. NeurIPS 2022 - [i17]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. CoRR abs/2202.12002 (2022) - [i16]Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Lichao Sun:
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation. CoRR abs/2203.09553 (2022) - [i15]Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. CoRR abs/2210.07297 (2022) - [i14]Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang:
MPCFormer: fast, performant and private Transformer inference with MPC. CoRR abs/2211.01452 (2022) - 2021
- [c10]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys 2021 - [c9]Hongyi Wang, Saurabh Agarwal, Dimitris S. Papailiopoulos:
Pufferfish: Communication-efficient Models At No Extra Cost. MLSys 2021 - [i13]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. CoRR abs/2103.00543 (2021) - [i12]Hongyi Wang, Saurabh Agarwal, Dimitris S. Papailiopoulos:
Pufferfish: Communication-efficient Models At No Extra Cost. CoRR abs/2103.03936 (2021) - [i11]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i10]Lingjiao Chen, Leshang Chen, Hongyi Wang, Susan B. Davidson, Edgar Dobriban:
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients. CoRR abs/2110.01595 (2021) - 2020
- [c8]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. ICLR 2020 - [c7]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. NeurIPS 2020 - [i9]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. CoRR abs/2002.06440 (2020) - [i8]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. CoRR abs/2007.05084 (2020) - [i7]Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr:
FedML: A Research Library and Benchmark for Federated Machine Learning. CoRR abs/2007.13518 (2020) - [i6]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. CoRR abs/2010.16248 (2020)
2010 – 2019
- 2019
- [c6]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS 2019: 10320-10330 - [c5]Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, Arun Kumar:
Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace. SIGMOD Conference 2019: 1885-1888 - [i5]Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. CoRR abs/1901.09671 (2019) - [i4]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR abs/1907.12205 (2019) - 2018
- [c4]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. ICML 2018: 902-911 - [c3]Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris:
The Effect of Network Width on the Performance of Large-batch Training. NeurIPS 2018: 9322-9332 - [c2]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. NeurIPS 2018: 9872-9883 - [i3]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Robust Distributed Training via Redundant Gradients. CoRR abs/1803.09877 (2018) - [i2]Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris:
The Effect of Network Width on the Performance of Large-batch Training. CoRR abs/1806.03791 (2018) - [i1]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. CoRR abs/1806.04090 (2018) - 2017
- [c1]Guru Subramani, Daniel Rakita, Hongyi Wang, Jordan Black, Michael R. Zinn, Michael Gleicher:
Recognizing actions during tactile manipulations through force sensing. IROS 2017: 4386-4393
Coauthor Index
Dimitris S. Papailiopoulos
aka: Dimitris Papailiopoulos
aka: Dimitris Papailiopoulos
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