郭丹丹

职称:教授、博士生导师

毕业院校:西安电子科技大学

email:guodandan@jlu.edu.cn

个人主页:https://dandanguo1993.github.io/Homepage

研究方向:模式识别机器学习

个人简介

郭丹丹,BETVLCTOR伟德官方网站准聘教授,博士生导师。研究方向为模式识别机器学习。理论上,包括概率模型构建与统计推断,元学习,算法公平性,最优传输理论等。所涉及的应用有图像生成及分类、文本分析、自然语言生成等。目前,专注于以数据为中心的机器学习算法研究,如小样本学习、不平衡分类等。在机器学习领域国际顶级会议(NeurIPS,ICML,ICLR)、顶级期刊(IEEE PAMI,IJCV,IEEE TNNLS)等发表共计16篇论文。

工作经历

2023.02 —— 至今 BETVLCTOR伟德官方网站 教授

2020.12—— 2023.02    香港中文大学(深圳) 博士后


教育经历

2014.09 —— 2020.08    西安电子科技大学 电子工程学院 信号与信息处理专业 硕博连读

2010.09 —— 2014.06    中北大学 信息与通信工程学院 光信息科学与技术专业 本科


科研项目

国家自然科学基金青年基金(青基):分布匹配驱动的不平衡分类样本扩充问题研究。



Discrete Dynamical Systems (DDS) for COVID-19 Forecast

https://dds-covid19.github.io/

Core Contributors


论文选

详见个人主页,近五年部分论文信息如下:(以下论文中,+为共同第一作者, *为通讯作者)

  1. Hangting Ye, Wei Fan , Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo∗ , Yi Chang∗. PTARL: Prototype-based Tabular Representation Learning via Space Calibration[C]//In International Conference on Learning Representations,2024. (ICLR,清华计算机评定A类会议, Spotlight)

  2. Jintong Gao, He Zhao, Zhuo Li, Dandan Guo*. Enhancing minority classes by mixing: an adaptive optimal transport approach for long-tailed classification[C]// Advances in Conference on Neural Information Processing Systems (2023).(NeurIPS, CCF A)

  3. Dandan Guo, Zhuo Li, Meixi zheng, He Zhao, Mingyuan Zhou, Hongyuan Zha. Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification[C]//Advances in Conference on Neural Information Processing Systems (2022).(NeurIPS,CCF A)

  4. Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou, Hongyuan Zha. Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport[C]//Advances in Conference on Neural Information Processing Systems (2022).(NeurIPS,CCF A)

  5.  Dandan Guo, Chaojie Wang , Baoxiang Wang, and Hongyuan Zha. Learning Fair Representations via Distance Correlation Minimization[J]//IEEE Transactions on Neural Networks and Learning Systems, 2022, doi: 10.1109/TNNLS.2022.3187165.(TNNLS,中科院1区)

  6. Dandan Guo+, Ruiying Lu+, Bo Chen and Mingyuan Zhou. Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning[J]//International Journal of Computer Vision , 2022, 130(8): 1920-1937. (IJCV,CCF-A)共同作者

  7. Dandan Guo, Long Tian, Minghe Zhang, Mingyuan Zhou, and Hongyuan Zha.Learning Prototype-oriented Set Representations for Meta-Learning[C]//In International Conference on Learning Representations,2022. (ICLR,清华计算机评定A类会议)

  8. Dongsheng Wang+, Dandan Guo+, He Zhao, Huangjie Zheng, Korawat Tanwisuth, Bo Chen and Mingyuan Zhou. Representing Mixtures of Word Embeddings with Topic Embeddings[C]//In International Conference on Learning Representations,2022. (ICLR,清华计算机评定A类会议)

  9. Dandan Guo, Bo Chen, Meixi Zheng and Hongwei Liu. SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model[J]// IEEE Transactions on Aerospace and Electronic Systems,57 (6), 4313-4328,2021.(TAES,中科院2区)

  10. Dandan Guo, Bo Chen, Ruiying Lu and Mingyuan Zhou. Recurrent Hierarchical Topic-Guided RNN for Language Generation [C]/In International Conference on Machine Learning, 2020. (ICML,CCF A)

  11. Dandan Guo, Bo Chen, Wenchao Chen and Mingyuan Zhou, Hongwei Liu. Variational Temporal Deep Generative Model for Radar HRRP Target Recognition[J]// IEEE Transactions on Signal Processing, 68, 5795-5809,2020. (TSP,中科院1区)

  12. Dandan Guo, Bo Chen, Hao Zhang and Mingyuan Zhou. "Deep Poisson Gamma Dynamical Systems[C]//Advances in Conference on Neural Information Processing Systems, 2018.(NeurIPS,CCF A)

  13. Jinpeng Hu, Dandan Guo, Yang Liu , Zhuo Li , Zhihong Chen, Xiang Wan1,Tsung-Hui Chang. A Simple yet Effective Subsequence-Enhanced Approach for Cross-Domain NER. Association for the Advancement of Artificial Intelligence,2023. (AAAI,CCF-A)

  14. Jinpeng Hu, He Zhao, Dandan Guo*, Xiang Wan*, Tsung-Hui Chang. " A Label-Aware Autoregressive Framework for Cross-Domain NER". Findings of NAACL (共同通信), 2022.

  15. Chuan Du, Yulai Cong, Lei Zhang, Dandan Guo, Song Wei. "A Practical Deceptive Jamming Method Based on Vulnerable Location Awareness Adversarial Attack for Radar HRRP Target Recognition" in IEEE Transactions on Information Forensics and Security(TIFS, SCI 一区,影响因子 7.178),2022.

  16. Hao Zhang, Bo Chen, Yulai Cong, Dandan Guo, Hongwei Liu, and Mingyuan Zhou. “Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, 机器学习顶级期刊,CCF-A 类期刊,影响因子:17.73), 2020.

  17. Chuan Du, Bo Chen, Bin Xu, Dandan Guo, and Hongwei Liu, “Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition,” Signal Processing (SP,信号处理期刊,SCI 二区,影响因子:4.086), vol. 158, pp. 176–189, 2019.

  18. Hao Zhang, Bo Chen, Dandan Guo, and Mingyuan Zhou. “WHAI. Weibull Autoencoding Inference for Deep Topic Modeling”, in International Conference on Learning Representations(ICLR,机器学习顶级国际会议,清华计算机评定 A 类会议), 2018.


社会兼职

会议审稿人: ICML 2019-2023,   NeurIPS 2019-2023, ICLR 2019-2023

期刊审稿人: JMLR, TNNLS,TSP等


招生要求

1、每年将招收多名硕士、博士研究生,欢迎highly motivated的员工报考。优先考虑有编程经验和科研经历的申请者。

2、踏实、认真、严谨,对工作与学习有较强的积极主动性。

3、在统计机器学习、数学、编程等任一方面有较深的功底和较浓的兴趣。

4、欢迎对科研有兴趣并希望继续深造的本科生。

编程经验较强、态度积极的员工将被优先考虑,欢迎报考本组研究生。你将有机会与国内外机器学习领域多名优秀学者一起合作,有机会参加国内外知名学术会议,期待你的进步与成长!


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