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Bingqing Song
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Fedavg converges to zero training loss linearly for overparameterized multi-layer neural networks
B Song, P Khanduri, X Zhang, J Yi, M Hong
International Conference on Machine Learning, 32304-32330, 2023
10*2023
To supervise or not to supervise: How to effectively learn wireless interference management models?
B Song, H Sun, W Pu, S Liu, M Hong
2021 IEEE 22nd International Workshop on Signal Processing Advances in …, 2021
92021
Distributed optimization for overparameterized problems: Achieving optimal dimension independent communication complexity
B Song, I Tsaknakis, CY Yau, HT Wai, M Hong
Advances in Neural Information Processing Systems 35, 6147-6160, 2022
52022
Building large machine learning models from small distributed models: A layer matching approach
X Zhang, B Song, M Honarkhah, J Ding, M Hong
Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022
22022
Unraveling the gradient descent dynamics of transformers
B Song, B Han, S Zhang, J Ding, M Hong
Advances in Neural Information Processing Systems 37, 92317-92351, 2024
12024
Transformer Based Approach for Wireless Resource Allocation Problems Involving Mixed Discrete and Continuous Variables
B Song, Z Zhou, C Li, D Guo, X Fu, M Hong
2023 IEEE 24th International Workshop on Signal Processing Advances in …, 2023
12023
Low-rank matrix completion for distributed ambient noise imaging systems
D Xu, B Song, Y Xie, SM Wu, FC Lin, WZ Song
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1059-1065, 2019
12019
Effectively Steer LLM To Follow Preference via Building Confident Directions
B Song, B Han, S Zhang, H Wang, H Fang, B Min, Y Wang, M Hong
arXiv preprint arXiv:2503.02989, 2025
2025
Building Large Models from Small Distributed Models: A Layer Matching Approach
X Zhang, B Song, M Honarkhah, J Dingl, M Hong
2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM …, 2024
2024
Privacy-preserving federated learning: algorithms and guarantees
X Zhang, X Chen, B Song, P Khanduri, M Hong
Federated Learning, 57-74, 2024
2024
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
B Song, H Sun, W Pu, S Liu, M Hong
arXiv preprint arXiv:2112.14011, 2021
2021
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