邬渊

职称:助理教授,博士生导师

毕业院校:加拿大卡尔顿大学

email:yuanwu@jlu.edu.cn

个人主页:

研究方向:迁移学习,机器学习泛化理论,大语言模型

个人简介

邬渊,澳门人威尼斯官方助理教授,博士生导师。研究方向为迁移学习,机器学习泛化理论。所涉及的应用有图像分类,图像语义分割,文本分类等。在AAAI,ECCV,ICASSP,AISTATS等国际会议发表文章5篇。


目前在探索大语言模型large language model (llm)。 本组与微软亚洲研究院,中科院自动化所,香港科技大学等单位合作发表首篇大语言模型评测综述论文 A Survey on Evaluation of Large Language Models. 


近几年计划的研究方向:(1) 大语言模型的评估 (The evaluation on large language models);(2) 可信大语言模型(Trustworthy large language models);(3)心理学与大语言模型的交叉研究(Applying psychology to improve large language models)。


欢迎对以上方向感兴趣的本科生、硕士生、博士生咨询!

(本组2024级博士名额已满)

工作经历

2022.11-至今 澳门人威尼斯官方 助理教授

教育经历

2018.09-2022.06   加拿大卡尔顿大学 计算机科学 博士 (导师:Dr. Ahemd El-Roby, Dr. Diana Inkpen.)

2015.09-2018.07   兰州大学 软件工程 硕士 (导师:李廉)

2008.09-2012.06   北京化工大学 计算机科学与技术 学士

论文选

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). (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.(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. (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). (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). (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. (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. (ICASSP, CCF-B, 清华B类)

[8] Chang, Y., Wang, X., Wang, J., Wu, Y., Zhu, K., Chen, H., ...& Xie, X. (2023). A survey on evaluation of large language models. arXiv preprint arXiv:2307.03109.


社会兼职

会议审稿人: AAAI, EMNLP, AISTATS

期刊审稿人: Information Science, IEEE transactions on Cybernetics

获奖情况

EACL 2021, the Second Workshop on Domain Adaptation for NLP, Best paper award.

AISTATS 2022, Outstanding reviewer award.

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