| SNrram: An efficient sparse neural network computation architecture based on resistive random-access memory P Wang, Y Ji, C Hong, Y Lyu, D Wang, Y Xie Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 130 | 2018 |
| Agic: Approximate gradient inversion attack on federated learning J Xu, C Hong, J Huang, LY Chen, J Decouchant 2022 41st International Symposium on Reliable Distributed Systems (SRDS), 12-22, 2022 | 38 | 2022 |
| Maverick matters: Client contribution and selection in federated learning J Huang, C Hong, Y Liu, LY Chen, S Roos Pacific-Asia Conference on Knowledge Discovery and Data Mining, 269-282, 2023 | 16 | 2023 |
| Is shapley value fair? improving client selection for mavericks in federated learning J Huang, C Hong, LY Chen, S Roos arXiv preprint arXiv:2106.10734, 2021 | 14 | 2021 |
| Tackling mavericks in federated learning via adaptive client selection strategy J Huang, C Hong, Y Liu, LY Chen, S Roos International Workshop on Trustable, Verifiable and Auditable Federated …, 2022 | 11 | 2022 |
| Online label aggregation: A variational bayesian approach C Hong, A Ghiassi, Y Zhou, R Birke, LY Chen WWW 2021, Proceedings of the Web Conference 2021, 1904-1915, 2021 | 8 | 2021 |
| Exploring and Exploiting Data-Free Model Stealing C Hong, J Huang, R Birke, LY Chen ECML PKDD 2023, Joint European Conference on Machine Learning and Knowledge …, 2023 | 3 | 2023 |
| Gradient inversion of federated diffusion models J Huang, C Hong, LY Chen, S Roos arXiv preprint arXiv:2405.20380, 2024 | 2 | 2024 |
| On dark knowledge for distilling generators C Hong, R Birke, PY Chen, LY Chen Pacific-Asia Conference on Knowledge Discovery and Data Mining, 235-247, 2024 | 2 | 2024 |
| MEGA: Model Stealing via Collaborative Generator-Substitute Networks C Hong, J Huang, LY Chen arXiv preprint arXiv:2202.00008, 2022 | 2 | 2022 |
| End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models T Younesian, C Hong, A Ghiassi, R Birke, LY Chen CogMI2020, 2020 | 2 | 2020 |
| Label aggregation via finding consensus between models C Hong, Y Zhou arXiv preprint arXiv:1807.07291, 2018 | 2 | 2018 |
| SFDDM: Single-fold Distillation for Diffusion models C Hong, J Huang, R Birke, D Epema, S Roos, LY Chen arXiv preprint arXiv:2405.14961, 2024 | 1 | 2024 |
| Generative Models for Learning from Crowds C Hong arXiv preprint arXiv:1706.03930, 2017 | 1 | 2017 |
| Single-fold Distillation for Diffusion models C Hong, J Huang, R Birke, D Epema, S Roos, LY Chen Joint European Conference on Machine Learning and Knowledge Discovery in …, 2025 | | 2025 |
| GIDM: Gradient Inversion of Federated Diffusion Models J Huang, C Hong, S Roos, LY Chen International Conference on Availability, Reliability and Security, 380-401, 2025 | | 2025 |
| Adversarial Knowledge Extraction via Steering Diffusion Models C Hong, J Huang, L Chen, R Birke International Conference on Neural Information Processing, 336-350, 2024 | | 2024 |
| Item Difficulty-Based Label Aggregation Models for Crowdsourcing C Hong CoRR, 2017 | | 2017 |
| PASCMP: A novel cache framework for data mining application C Hong, H Wang, D Wang 2016 2nd IEEE International Conference on Computer and Communications (ICCC …, 2016 | | 2016 |
| 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)| 978-1-7281-4144-2/20/$31.00© 2020 IEEE| DOI: 10.1109/COGMI50398. 2020.00039 T Abdelzaher, GD Abowd, A Alten, J Bae, R Bagwe, L Barbaglia, ... | | |
