| Heterogeneity-aware distributed parameter servers J Jiang, B Cui, C Zhang, L Yu Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 144 | 2017 |
| Sketchml: Accelerating distributed machine learning with data sketches J Jiang, F Fu, T Yang, B Cui Proceedings of the 2018 International Conference on Management of Data, 1269 …, 2018 | 69 | 2018 |
| Angel: a new large-scale machine learning system J Jiang, L Yu, J Jiang, Y Liu, B Cui National Science Review 5 (2), 216-236, 2018 | 69 | 2018 |
| Dimboost: Boosting gradient boosting decision tree to higher dimensions J Jiang, B Cui, C Zhang, F Fu Proceedings of the 2018 International Conference on Management of Data, 1363 …, 2018 | 30 | 2018 |
| Ps2: Parameter server on spark Z Zhang, B Cui, Y Shao, L Yu, J Jiang, X Miao Proceedings of the 2019 International Conference on Management of Data, 376-388, 2019 | 24 | 2019 |
| Towards demystifying serverless machine learning training J Jiang, S Gan, Y Liu, F Wang, G Alonso, A Klimovic, A Singla, W Wu, ... Proceedings of the 2021 International Conference on Management of Data, 857-871, 2021 | 22 | 2021 |
| TencentBoost: a gradient boosting tree system with parameter server J Jiang, J Jiang, B Cui, C Zhang 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 281-284, 2017 | 22 | 2017 |
| Snapshot boosting: a fast ensemble framework for deep neural networks W Zhang, J Jiang, Y Shao, B Cui Science China Information Sciences 63 (1), 1-12, 2020 | 21 | 2020 |
| Servers J Jiang, B Cui, C Zhang, LYHAD Parameter 2017 ACM International Conference on Management of Data (SIGMOD) 10 (3035918 …, 2017 | 21 | 2017 |
| Openbox: A generalized black-box optimization service Y Li, Y Shen, W Zhang, Y Chen, H Jiang, M Liu, J Jiang, J Gao, W Wu, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 19 | 2021 |
| An experimental evaluation of large scale GBDT systems F Fu, J Jiang, Y Shao, B Cui arXiv preprint arXiv:1907.01882, 2019 | 18 | 2019 |
| Reliable data distillation on graph convolutional network W Zhang, X Miao, Y Shao, J Jiang, L Chen, O Ruas, B Cui Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 16 | 2020 |
| Mllib*: Fast training of glms using spark mllib Z Zhang, J Jiang, W Wu, C Zhang, L Yu, B Cui 2019 IEEE 35th International Conference on Data Engineering (ICDE), 1778-1789, 2019 | 16 | 2019 |
| Don’t waste your bits! squeeze activations and gradients for deep neural networks via tinyscript F Fu, Y Hu, Y He, J Jiang, Y Shao, C Zhang, B Cui International Conference on Machine Learning, 3304-3314, 2020 | 14 | 2020 |
| Efficient automatic CASH via rising bandits Y Li, J Jiang, J Gao, Y Shao, C Zhang, B Cui Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4763-4771, 2020 | 13 | 2020 |
| Sparse gradient compression for distributed SGD H Sun, Y Shao, J Jiang, B Cui, K Lei, Y Xu, J Wang International Conference on Database Systems for Advanced Applications, 139-155, 2019 | 13 | 2019 |
| SKCompress: compressing sparse and nonuniform gradient in distributed machine learning J Jiang, F Fu, T Yang, Y Shao, B Cui The VLDB Journal 29 (5), 945-972, 2020 | 9 | 2020 |
| PSGraph: How Tencent trains extremely large-scale graphs with Spark? J Jiang, P Xiao, L Yu, X Li, J Cheng, X Miao, Z Zhang, B Cui 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1549-1557, 2020 | 9 | 2020 |
| Efficient diversity-driven ensemble for deep neural networks W Zhang, J Jiang, Y Shao, B Cui 2020 IEEE 36th International Conference on Data Engineering (ICDE), 73-84, 2020 | 8 | 2020 |
| VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition Y Li, Y Shen, W Zhang, J Jiang, B Ding, Y Li, J Zhou, Z Yang, W Wu, ... arXiv preprint arXiv:2107.08861, 2021 | 7 | 2021 |
Bin CUIProfessor of Computer Science, Peking UniversityVerified email at pku.edu.cn
