| A Survey on Graph Structure Learning: Progress and Opportunities Y Zhu, W Xu, J Zhang, Y Du, J Zhang, Q Liu, C Yang, S Wu arXiv preprint arXiv:2103.03036, 2021 | 76* | 2021 |
| WRENCH: A Comprehensive Benchmark for Weak Supervision J Zhang, Y Yu, Y Li, Y Wang, Y Yang, M Yang, A Ratner 35th Conference on Neural Information Processing Systems (NeurIPS 2021 …, 2021 | 39 | 2021 |
| Relation learning on social networks with multi-modal graph edge variational autoencoders C Yang, J Zhang, H Wang, S Li, M Kim, M Walker, Y Xiao, J Han Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 28 | 2020 |
| Co-embedding network nodes and hierarchical labels with taxonomy based generative adversarial networks C Yang, J Zhang, J Han 2020 IEEE International Conference on Data Mining (ICDM), 721-730, 2020 | 27 | 2020 |
| A Survey on Programmatic Weak Supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022 | 20 | 2022 |
| Neural embedding propagation on heterogeneous networks C Yang, J Zhang, J Han 2019 IEEE International Conference on Data Mining (ICDM), 698-707, 2019 | 17 | 2019 |
| Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph? H Wang, J Zhang, Q Zhu, W Huang arXiv preprint arXiv:2211.10890, 2022 | 11* | 2022 |
| Taxonomy Completion via Triplet Matching Network J Zhang, X Song, Y Zeng, J Chen, J Shen, Y Mao, L Li Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 11 | 2021 |
| Neural Concept Map Generation for Effective Document Classification with Interpretable Structured Summarization C Yang, J Zhang, H Wang, B Li, J Han Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 10 | 2020 |
| Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming CY Hsieh, J Zhang, A Ratner arXiv preprint arXiv:2203.01382, 2022 | 8 | 2022 |
| Creating Training Sets via Weak Indirect Supervision J Zhang, B Wang, X Song, Y Wang, Y Yang, J Bai, A Ratner International Conference on Learning Representations (ICLR 2022), 2021 | 8 | 2021 |
| A Survey on Deep Graph Generation: Methods and Applications Y Zhu, Y Du, Y Wang, Y Xu, J Zhang, Q Liu, S Wu arXiv preprint arXiv:2203.06714, 2022 | 7 | 2022 |
| Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion X Song, J Shen, J Zhang, J Han SSL@WWW2021, 2021 | 5 | 2021 |
| AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models Y Yu, L Kong, J Zhang, R Zhang, C Zhang Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 4* | 2022 |
| Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD B Wang, H Zhang, J Zhang, Q Meng, W Chen, TY Liu 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 4 | 2021 |
| Cubenet: Multi-facet hierarchical heterogeneous network construction, analysis, and mining C Yang, D Teng, S Liu, S Basu, J Zhang, J Shen, C Zhang, J Shang, ... The ACM International Conference on Knowledge Discovery and Data Mining (KDD …, 2019 | 4 | 2019 |
| Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach Y Yu, R Zhang, R Xu, J Zhang, J Shen, C Zhang arXiv preprint arXiv:2209.06995, 2022 | 3 | 2022 |
| Understanding Programmatic Weak Supervision via Source-aware Influence Function J Zhang, H Wang, CY Hsieh, A Ratner arXiv preprint arXiv:2205.12879, 2022 | 3 | 2022 |
| ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration Y Du, X Liu, S Liu, J Zhang, B Zhou ICLR2022 Machine Learning for Drug Discovery, 2022 | 3* | 2022 |
| Learning Hyper Label Model for Programmatic Weak Supervision R Wu, SE Chen, J Zhang, X Chu arXiv preprint arXiv:2207.13545, 2022 | 1* | 2022 |
Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisAdresse e-mail validée de cs.uiuc.edu
