详见个人Google scholar:https://scholar.google.com/citations?user=KVeRu2QAAAAJ&hl=zh-CN
部分论文信息如下：
[1] Wu, Y., & Guo, Y. (2020, April). Dual adversarial co-learning for multi-domain text classification. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 6438-6445). (第一作者，AAAI, CCF-A, 清华A类)
[2] Wu, Y., Inkpen, D., & El-Roby, A. (2020). Dual mixup regularized learning for adversarial domain adaptation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16 (pp. 540-555). Springer International Publishing. (第一作者，ECCV, CCF-B, 清华A类)
[3] Wu, Y., Inkpen, D., & El-Roby, A. (2021, June). Mixup regularized adversarial networks for multi-domain text classification. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7733-7737). IEEE. (第一作者，ICASSP, CCF-B, 清华B类)
[4] Wu, Y., Inkpen, D., & El-Roby, A. (2021). Towards category and domain alignment: Category-invariant feature enhancement for adversarial domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision workshops (pp. 132-141). (第一作者， ICCV workshop)
[5] Wu, Y., Inkpen, D., & El-Roby, A. (2021, April). Conditional Adversarial Networks for Multi-Domain Text Classification. In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 16-27). (第一作者， EACL workshop)
[6] Wu, Y., Inkpen, D., & El-Roby, A. (2022, May). Co-regularized adversarial learning for multi-domain text classification. In International Conference on Artificial Intelligence and Statistics (pp. 6690-6701). PMLR. (第一作者，AISTATS, CCF-C, 清华B类)
[7] Wu, Y., Inkpen, D., & El-Roby, A. (2022, May). Maximum Batch Frobenius Norm for Multi-Domain Text Classification. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3763-3767). IEEE. (第一作者，ICASSP, CCF-B, 清华B类)
[8] Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., ... & Xie, X. (2023). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology. （通讯作者）
[9] Hu, J., & Wu, Y. (2024, April). Regularized Conditional Alignment for Multi-Domain Text Classification. In ICASSP 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5645-5649). IEEE. (通讯作者，ICASSP, CCF-B, 清华B类)
[10] Wang, X., Li, C., Chang, Y., Wang, J., & Wu, Y. (2024, August). NegativePrompt: leveraging psychology for large language models enhancement via negative emotional stimuli. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 6504-6512). (通讯作者，IJCAI, CCF-A, 清华B类)
[11] Zhou, Y., Guo, C., Wang, X., Chang, Y., & Wu, Y. (2024). A Survey on Data Augmentation in Large Model Era. arXiv preprint arXiv:2401.15422.（通讯作者）
[12] Xia, T., Yu, B., Wu, Y., Chang, Y., & Zhou, C. (2024, August). Language Models can Evaluate Themselves via Probability Discrepancy. In Findings of the Association for Computational Linguistics ACL 2024 (pp. 4889-4901). (通讯作者，ACL, CCF-A, 清华A类)
[13] Wu, Y. (2024, November). Margin Discrepancy-Based Adversarial Training for Multi-Domain Text Classification. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 170-182). Singapore: Springer Nature Singapore. (独立作者, NLPCC, CCF-C)
[14] Li, J., Li, J., Wang, Y., Chang, Y., & Wu, Y. (2025). StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following. This paper will appear at ACL Findings 2025. (通讯作者，ACL, CCF-A, 清华A类)
[15] Li, Z., Xia, T., Chang, Y., & Wu, Y. (2025). A Survey of RWKV. This paper has been accepted by Neurocomputing. (通讯作者, Neurocomputing, CCF-C, 清华B类, 中科院二区)
[16] Xia, T., Li, Y., Wu, Y., & Chang, Y. (2025). Selective Fine-Tuning for Large Language Models via Matrix Nuclear Norm. This paper has been accepted by Information Processing & Management. (通讯作者, IPM, CCF-B, 清华B类, 中科院一区)
[17] Xia, T., Yu, B., Dang, K., Yang, A., Wu, Y., Tian, Y., ... & Lin, J. (2024). Rethinking data selection at scale: Random selection is almost all you need. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[18] Li, Y., Xia, T., Chang, Y., & Wu, Y. (2024). Large Language Model Evaluation via Matrix Nuclear-Norm. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[19] Chang, Y., Guo, C., Chang, Y., & Wu, Y. (2025). LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[20] Guo, C., Chang, Y., & Wu, Y.(2025). NLoRA: Nystrom-Initiated Low-Rank Adaptation for Large Language Models. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[21] Liu, J., Chang, Y., & Wu, Y. (2025). R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[22] Li, J., Li, G., Chang, Y., & Wu, Y. (2025). Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models. This paper will appear at EMNLP Findings 2025. (通讯作者, EMNLP, CCF-B, 清华A类)
[23] Li, G., & Wu, Y. (2025). Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation. This paper will appear at PRCV 2025. (通讯作者, PRCV, CCF-C)
