| An incentive auction for heterogeneous client selection in federated learning J Pang, J Yu, R Zhou, JCS Lui IEEE Transactions on Mobile Computing 22 (10), 5733-5750, 2022 | 59 | 2022 |
| LLM Unlearning via Loss Adjustment with Only Forget Data Y Wang, J Wei, CY Liu, J Pang, Q Liu, AP Shah, Y Bao, Y Liu, W Wei ICLR 2025, 2024 | 40 | 2024 |
| A truthful procurement auction for incentivizing heterogeneous clients in federated learning R Zhou, J Pang, Z Wang, JCS Lui, Z Li 2021 IEEE 41st International Conference on Distributed Computing Systems …, 2021 | 34 | 2021 |
| Online scheduling algorithm for heterogeneous distributed machine learning jobs R Zhou, J Pang, Q Zhang, C Wu, L Jiao, Y Zhong, Z Li IEEE Transactions on Cloud Computing 11 (2), 1514-1529, 2022 | 29 | 2022 |
| Improving Data Efficiency via Curating LLM-Driven Rating Systems J Pang, J Wei, AP Shah, Z Zhu, Y Wang, C Qian, Y Liu, Y Bao, W Wei ICLR 2025, 2024 | 19 | 2024 |
| Automatic dataset construction (adc): Sample collection, data curation, and beyond M Liu, Z Di, J Wei, Z Wang, H Zhang, R Xiao, H Wang, J Pang, H Chen, ... arXiv preprint arXiv:2408.11338, 2024 | 18 | 2024 |
| Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning J Pang, N Di, Z Zhu, J Wei, H Cheng, C Qian, Y Liu ICML 2025, 2025 | 16 | 2025 |
| DPS: Dynamic pricing and scheduling for distributed machine learning jobs in edge-cloud networks R Zhou, N Wang, Y Huang, J Pang, H Chen IEEE Transactions on Mobile Computing 22 (11), 6377-6393, 2022 | 16 | 2022 |
| Fairness Without Harm: An Influence-Guided Active Sampling Approach J Pang, J Wang, Z Zhu, Y Yao, C Qian, Y Liu NeurIPS 2024, 2024 | 13* | 2024 |
| Online scheduling algorithms for unbiased distributed learning over wireless edge networks J Pang, Z Han, R Zhou, H Tan, Y Cao Journal of Systems Architecture 131, 102673, 2022 | 10 | 2022 |
| Towards practical overlay networks for decentralized federated learning Y Hua, J Pang, X Zhang, Y Liu, X Shi, B Wang, Y Liu, C Qian 2024 IEEE 32nd International Conference on Network Protocols (ICNP), 1-11, 2024 | 6 | 2024 |
| Eris: An online auction for scheduling unbiased distributed learning over edge networks J Pang, Z Han, R Zhou, R Zhang, JCS Lui, H Chen IEEE Transactions on Mobile Computing 23 (6), 7196-7209, 2023 | 5 | 2023 |
| Online scheduling unbiased distributed learning over wireless edge networks Z Han, R Zhou, J Pang, Y Cao, H Tan 2021 IEEE 27th International Conference on Parallel and Distributed Systems …, 2021 | 4 | 2021 |
| DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning Y Wang, CY Liu, Q Liu, J Pang, W Wei, Y Bao, Y Liu arXiv preprint arXiv:2511.05784, 2025 | 1 | 2025 |
| Evaluating llm-corrupted crowdsourcing data without ground truth Y Zhang, J Pang, Z Zhu, Y Liu arXiv preprint arXiv:2506.06991, 2025 | 1 | 2025 |
| Supervised Fine-Tuning on Ambiguous Preference Pairs Boosts LLM Alignment J Pang, Z Zhu, N Di, Y Zhang, Y Wang, C Qian, Y Liu | 1 | 2025 |
| ENTP: Enhancing Low-Quality SFT Data via Neural-Symbolic Text Purge-Mix Z Yang, L Li, N Di, J Pang, Y Zhou, H Cheng, B Han, J Wei arXiv preprint arXiv:2510.23160, 2025 | | 2025 |
| Incentivizing High-quality Participation From Federated Learning Agents J Pang, J Wei, Y Hua, C Qian, Y Liu arXiv preprint arXiv:2506.16731, 2025 | | 2025 |
| Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth Y Zhang, J Pang, Z Zhu, Y Liu The Thirty-ninth Annual Conference on Neural Information Processing Systems, 0 | | |
| Incentivizing Data Collection from Heterogeneous Clients in Federated Learning J Pang, J Wei, C Qian, Y Liu | | |
Yang LiuComputer Science and Engineering, UC Santa CruzVerified email at ucsc.edu
