| Federated Learning with Matched Averaging H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni ICLR 2020 - International Conference on Learning Representations, 2020 | 1377 | 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 | 709 | 2020 |
| 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 | 678* | 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 | 419 | 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 | 411 | 2021 |
| Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018 | 307* | 2018 |
| Position: TrustLLM: Trustworthiness in large language models Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ... International Conference on Machine Learning, 20166-20270, 2024 | 294* | 2024 |
| 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 | 141 | 2019 |
| 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 | 75 | 2022 |
| Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019 | 65 | 2019 |
| Pufferfish: Communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021 | 59 | 2021 |
| Llm360: Towards fully transparent open-source llms Z Liu, A Qiao, W Neiswanger, H Wang, B Tan, T Tao, J Li, Y Wang, S Sun, ... arXiv preprint arXiv:2312.06550, 2023 | 55 | 2023 |
| 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 | 53 | 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 | 47* | 2021 |
| Rare Gems: Finding Lottery Tickets at Initialization K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ... NeurIPS 2022, 2022 | 44 | 2022 |
| Slimpajama-dc: Understanding data combinations for llm training Z Shen, T Tao, L Ma, W Neiswanger, Z Liu, H Wang, B Tan, J Hestness, ... arXiv preprint arXiv:2309.10818, 2023 | 42 | 2023 |
| Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation K Zhang, Y Wang, H Wang, L Huang, C Yang, X Chen, L Sun arXiv preprint arXiv:2203.09553, 2022 | 39 | 2022 |
| Fusing models with complementary expertise H Wang, FM Polo, Y Sun, S Kundu, E Xing, M Yurochkin arXiv preprint arXiv:2310.01542, 2023 | 31 | 2023 |
| 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 | 24 | 2018 |
| AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness D Li, H Wang, E Xing, H Zhang NeurIPS 2022, 2022 | 20 | 2022 |
Dimitris PapailiopoulosResearcher at Microsoft Research & Assoc. Professor at University of Wisconsinpapail.io의 이메일 확인됨