ThunderSVM: A fast SVM library on GPUs and CPUs Z Wen, J Shi, Q Li, B He, J Chen The Journal of Machine Learning Research 19 (1), 797-801, 2018 | 84 | 2018 |
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection Q Li, Z Wen, Z Wu, S Hu, N Wang, B He arXiv preprint arXiv:1907.09693, 2019 | 60 | 2019 |
Practical Federated Gradient Boosting Decision Trees Q Li, Z Wen, B He AAAI 2020, 2020 | 21 | 2020 |
Exploiting GPUs for efficient gradient boosting decision tree training Z Wen, J Shi, B He, J Chen, K Ramamohanarao, Q Li IEEE Transactions on Parallel and Distributed Systems 30 (12), 2706-2717, 2019 | 12 | 2019 |
Privacy-Preserving Gradient Boosting Decision Trees Q Li, Z Wu, Z Wen, B He AAAI 2020, 2020 | 9 | 2020 |
ThunderGBM: Fast GBDTs and Random Forests on GPUs Z Wen, H Liu, J Shi, Q Li, B He, J Chen The Journal of Machine Learning Research (JMLR), 2020 | 7 | 2020 |
The oarf benchmark suite: Characterization and implications for federated learning systems S Hu, Y Li, X Liu, Q Li, Z Wu, B He arXiv preprint arXiv:2006.07856, 2020 | 4 | 2020 |
Adaptive Kernel Value Caching for SVM Training Q Li, Z Wen, B He IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019 | 3 | 2019 |
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer Q Li, B He, D Song arXiv preprint arXiv:2010.01017, 2020 | 1 | 2020 |
Federated Learning on Non-IID Data Silos: An Experimental Study Q Li, Y Diao, Q Chen, B He arXiv preprint arXiv:2102.02079, 2021 | | 2021 |