| Augmenting operations research with auto-formulation of optimization models from problem descriptions R Ramamonjison, H Li, TT Yu, S He, V Rengan, A Banitalebi-Dehkordi, ... arXiv preprint arXiv:2209.15565, 2022 | 24* | 2022 |
| Nl4opt competition: Formulating optimization problems based on their natural language descriptions R Ramamonjison, T Yu, R Li, H Li, G Carenini, B Ghaddar, S He, ... NeurIPS 2022 Competition Track, 189-203, 2023 | 13 | 2023 |
| On the convergence of quantized parallel restarted SGD for serverless learning F Wu, S He, Y Yang, H Wang, Z Qu, S Guo arXiv preprint arXiv:2004.09125 130, 2020 | 7* | 2020 |
| Anchor sampling for federated learning with partial client participation F Wu, S Guo, Z Qu, S He, Z Liu, J Gao International Conference on Machine Learning, 37379-37416, 2023 | 5 | 2023 |
| Sign bit is enough: A learning synchronization framework for multi-hop all-reduce with ultimate compression F Wu, S He, S Guo, Z Qu, H Wang, W Zhuang, J Zhang Proceedings of the 59th ACM/IEEE Design Automation Conference, 193-198, 2022 | 5 | 2022 |
| Gluefl: Reconciling client sampling and model masking for bandwidth efficient federated learning S He, Q Yan, F Wu, L Wang, M Lécuyer, I Beschastnikh Proceedings of Machine Learning and Systems 5, 695-707, 2023 | 4 | 2023 |
| Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning Z Jiang, P Ye, S He, W Wang, R Chen, B Li arXiv preprint arXiv:2401.02880, 2024 | | 2024 |
Feijie WuPurdue University在 purdue.edu 的电子邮件经过验证
