Publications 学术论文
2027
J. Hu, L. Jiang*, and W. Zhang. Dual-View Label Integration for Crowdsourcing. Frontiers of Computer Science, DOI: 10.1007/s11704-026-52044-5.
L. Yu, W. Zhang, and L. Jiang*. Random Forest-based Weighted Majority Voting for Crowdsourcing. Frontiers of Computer Science, 2027, 21(3): 2103603.
2026
W. Zhang, L. Jiang*, C. Li and S. Si. MA$^3$S: Model-Agnostic Active Annotation Strategy for Crowdsourcing. In: Proceedings of the 43rd International Conference on Machine Learning, ICML 2026. (CCF-A)
2025
W. Zhang, L. Jiang*, and C. Li. ELDP: Enhanced Label Distribution Propagation for Crowdsourcing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(3): 1850-1862. (CCF-A)
W. Zhang, L. Jiang*, and C. Li. TLLC: Transfer Learning-based Label Completion for Crowdsourcing. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 75178-75191. (CCF-A, Spotlight)
C. Li, L. Jiang*, W. Zhang, L. Yu, and H. Zhang. Instance Correlation Graph-based Naive Bayes. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 35021-35033. (CCF-A, Spotlight)
T. Wu, L. Jiang*, W. Zhang, and C. Li. Label Distribution Propagation-based Label Completion for Crowdsourcing. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 67369-67381. (CCF-A)
J. Li, L. Jiang*, and W. Zhang. Label Consistency-based Ground Truth Inference for Crowdsourcing. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(5): 9408-9421. (CAAI-A)
X. Wu, L. Jiang*, W. Zhang, and C. Li. Worker Similarity-based Label Completion for Crowdsourcing. IEEE Transactions on Big Data, 2025, 11(2): 710-721.
Q. Ji, L. Jiang, W. Zhang, and C. Li. Learning from Crowds by Class-specific Instance Weighting. IEEE Transactions on Emerging Topics in Computational Intelligence, 2025, 9(6): 4015-4025.
H. Zhang, L. Jiang*, W. Zhang, and G. I. Webb. Dual-View Learning from Crowds. ACM Transactions on Knowledge Discovery from Data, 2025, 19(3): 61.
2024
W. Zhang, L. Jiang*, Z. Chen, and C. Li. FNNWV: Farthest-Nearest Neighbor-based Weighted Voting for Class-Imbalanced Crowdsourcing.. Science China Information Sciences, 2024, 67(10): 202102. (CCF-A)
W. Zhang, L. Jiang*, and C. Li. KFNN: K-Free Nearest Neighbor For Crowdsourcing. In: Proceedings of the 38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024, Advances in Neural Information Processing Systems 37: 116493-116512. (CCF-A)
W. Zhang, L. Jiang*, and C. Li. IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing. In: Proceedings of the 38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024, Advances in Neural Information Processing Systems 37: 85722-85741. (CCF-A)
B. Yang, L. Jiang*, and W. Zhang. Probabilistic Matrix Factorization-based Three-stage Label Completion for Crowdsourcing. In: Proceedings of the 24th IEEE International Conference on Data Mining, ICDM 2024, pp. 540-549.
J. Li, L. Jiang*, X. Wu, and W. Zhang. Learning from Crowds with Dual-View K-Nearest Neighbor. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024, PMLR 244: 2238-2249. (CAAI-A)
J. Li, L. Jiang*, C. Li, and W. Zhang. Label Consistency-based Worker Filtering for Crowdsourcing. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024, PMLR 244: 2226-2237. (CAAI-A)
Z. Chen, L. Jiang*, W. Zhang, and C. Li. Weighted Adversarial Learning from Crowds. IEEE Transactions on Services Computing, 2024, 17(6): 4467-4480. (CCF-A)
Y. Hu, L. Jiang*, and W. Zhang. Worker Similarity-based Noise Correction for Crowdsourcing. Information Systems, 2024, 121: 102321.
L. Ren, L. Jiang*, W. Zhang, and C. Li. Label Distribution Similarity-based Noise Correction for Crowdsourcing. Frontiers of Computer Science, 2024, 18(5): 185323.
2023
X. Wu, L. Jiang*, W. Zhang, and C. Li. Three-way Decision-based Noise Correction for Crowdsourcing. International Journal of Approximate Reasoning, 2023, 160: 108973.
Q. Ji, L. Jiang*, and W. Zhang. Instance Weighting-based Noise Correction for Crowdsourcing. In: Proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, LNAI 14089: 285–297.
Q. Ji, L. Jiang*, and W. Zhang. Dual-View Noise Correction for Crowdsourcing. IEEE Internet of Things Journal, 2023, 10(13): 11804-11812.
H. Zhang, L. Jiang*, W. Zhang, and C. Li. Multi-view Attribute Weighted Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(7): 7291-7302. (CCF-A)
张文钧, 蒋良孝*, 张欢, 胡成玉. 一种基于偏差-方差权衡的贝叶斯分类学习框架. 中国科学: 信息科学, 2023, 53(6): 1078-1095.（CCF-A中文期刊）
2022
张文钧, 蒋良孝*, 张欢. 基于特征增广的生成-判别混合模型构建方法. 中国科学: 信息科学, 2022, 52(10): 1792-1807.（CCF-A中文期刊）
2021
张文钧, 蒋良孝*, 张欢, 陈龙. 一种双层贝叶斯模型: 随机森林朴素贝叶斯. 计算机研究与发展, 2021, 58(9): 2040-2051.（CCF-A中文期刊）