Qinbin Li

Orcid: 0000-0002-6539-6443

According to our database1, Qinbin Li authored at least 27 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams.
Proc. VLDB Endow., February, 2024

Federated Learning with New Knowledge: Fundamentals, Advances, and Futures.
CoRR, 2024

Exploiting Label Skews in Federated Learning with Model Concatenation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.
IEEE Trans. Knowl. Data Eng., April, 2023

DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning.
Proc. ACM Manag. Data, 2023

Effective and Efficient Federated Tree Learning on Hybrid Data.
CoRR, 2023

SoK: Privacy-Preserving Data Synthesis.
CoRR, 2023

Communication-Efficient Generalized Neuron Matching for Federated Learning.
Proceedings of the 52nd International Conference on Parallel Processing, 2023

Adversarial Collaborative Learning on Non-IID Features.
Proceedings of the International Conference on Machine Learning, 2023

Towards Addressing Label Skews in One-Shot Federated Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks.
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems.
ACM Trans. Intell. Syst. Technol., 2022

Practical Vertical Federated Learning with Unsupervised Representation Learning.
CoRR, 2022

UniFed: A Benchmark for Federated Learning Frameworks.
CoRR, 2022

A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Federated Learning on Non-IID Data Silos: An Experimental Study.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

2021
Challenges and Opportunities of Building Fast GBDT Systems.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Practical One-Shot Federated Learning for Cross-Silo Setting.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Model-Contrastive Federated Learning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Adaptive Kernel Value Caching for SVM Training.
IEEE Trans. Neural Networks Learn. Syst., 2020

ThunderGBM: Fast GBDTs and Random Forests on GPUs.
J. Mach. Learn. Res., 2020

Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer.
CoRR, 2020

Privacy-Preserving Gradient Boosting Decision Trees.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Practical Federated Gradient Boosting Decision Trees.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Exploiting GPUs for Efficient Gradient Boosting Decision Tree Training.
IEEE Trans. Parallel Distributed Syst., 2019

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.
CoRR, 2019

2018
ThunderSVM: A Fast SVM Library on GPUs and CPUs.
J. Mach. Learn. Res., 2018


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