Hongyi Wang

According to our database, Hongyi Wang authored at least 36 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

TrustLLM: Trustworthiness in Large Language Models.

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 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

CoRR, 2024

2023

LLM360: Towards Fully Transparent Open-Source LLMs.

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

CoRR, 2023

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices.

Minghao Yan, Hongyi Wang, Shivaram Venkataraman

CoRR, 2023

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs.

Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu

CoRR, 2023

Fusing Models with Complementary Expertise.

Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin

CoRR, 2023

SlimPajama-DC: Understanding Data Combinations for LLM Training.

Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing

CoRR, 2023

Maestro: Uncovering Low-Rank Structures via Trainable Decomposition.

Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang

CoRR, 2023

Cuttlefish: Low-Rank Model Training without All the Tuning.

Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris S. Papailiopoulos

CoRR, 2023

Memory-adaptive Depth-wise Heterogenous Federated Learning.

Kai Zhang, Yutong Dai, Hongyi Wang, Eric P. Xing, Xun Chen, Lichao Sun

CoRR, 2023

Does compressing activations help model parallel training?

Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman

CoRR, 2023

FedNAR: Federated Optimization with Normalized Annealing Regularization.

Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC.

Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach.

Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

MPCFormer: fast, performant and private Transformer inference with MPC.

Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang

CoRR, 2022

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation.

Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Lichao Sun

CoRR, 2022

Rare Gems: Finding Lottery Tickets at Initialization.

Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris S. Papailiopoulos

CoRR, 2022

Rare Gems: Finding Lottery Tickets at Initialization.

Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness.

Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Utility of Gradient Compression in Distributed Training Systems.

Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos

Proceedings of Machine Learning and Systems 2022, 2022

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation.

Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021

Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients.

Lingjiao Chen, Leshang Chen, Hongyi Wang, Susan B. Davidson, Edgar Dobriban

CoRR, 2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Pufferfish: Communication-efficient Models At No Extra Cost.

Hongyi Wang, Saurabh Agarwal, Dimitris S. Papailiopoulos

Proceedings of Machine Learning and Systems 2021, 2021

Adaptive Gradient Communication via Critical Learning Regime Identification.

Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos

Proceedings of Machine Learning and Systems 2021, 2021

2020

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.

Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos

CoRR, 2020

FedML: A Research Library and Benchmark for Federated Machine Learning.

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

CoRR, 2020

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning.

Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Learning with Matched Averaging.

Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding.

Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos

CoRR, 2019

Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace.

Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, Arun Kumar

Proceedings of the 2019 International Conference on Management of Data, 2019

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation.

Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

DRACO: Robust Distributed Training via Redundant Gradients.

Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos

CoRR, 2018

ATOMO: Communication-efficient Learning via Atomic Sparsification.

Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Effect of Network Width on the Performance of Large-batch Training.

Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients.

Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Recognizing actions during tactile manipulations through force sensing.

Guru Subramani, Daniel Rakita, Hongyi Wang, Jordan Black, Michael R. Zinn, Michael Gleicher

Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017