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., & Wu, Y. (2024). NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli. This paper will appear at IJCAI 2024. (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). Language Models can Evaluate Themselves via Probability Discrepancy. This paper will appear at ACL Findings 2024. (通讯作者，ACL, CCF-A, 清华A类)
[13] Wu, Y. (2024). Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification. This paper will appear at NLPCC 2024. (独立作者, NLPCC, CCF-C)
