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)
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)
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）
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)
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)
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
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)
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)
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)
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）
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)
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）
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)
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.
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.
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.
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.
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.
