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Qinbin Li
Qinbin Li
Verified email at comp.nus.edu.sg - Homepage
Title
Cited by
Cited by
Year
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, Y Li, X Liu, B He
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019
211*2019
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
1392018
Practical Federated Gradient Boosting Decision Trees
Q Li, Z Wen, B He
AAAI 2020, 2020
642020
Model-Contrastive Federated Learning
Q Li, B He, D Song
CVPR 2021, 2021
542021
Federated learning on non-iid data silos: An experimental study
Q Li*, Y Diao*, Q Chen, B He
ICDE 2022, 2021
522021
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
282019
Privacy-Preserving Gradient Boosting Decision Trees
Q Li, Z Wu, Z Wen, B He
AAAI 2020, 2020
272020
Practical One-Shot Federated Learning for Cross-Silo Setting
Q Li, B He, D Song
IJCAI 2021, 2021
18*2021
The oarf benchmark suite: Characterization and implications for federated learning systems
S Hu, Y Li, X Liu, Q Li, Z Wu, B He
ACM Transactions on Intelligent Systems and Technology (TIST), 2021
162021
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
142020
Adaptive Kernel Value Caching for SVM Training
Q Li, Z Wen, B He
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
82019
Adversarial Collaborative Learning on Non-IID Features
Q Li, B He, D Song
2021
Exploiting Record Similarity for Practical Vertical Federated Learning
Z Wu, Q Li, B He
arXiv preprint arXiv:2106.06312, 2021
2021
Challenges and Opportunities of Building Fast GBDT Systems
Z Wen, Q Li, B He, B Cui
IJCAI 2021 Survey, 2021
2021
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