| AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework Q Wu, G Bansal, J Zhang, Y Wu, S Zhang, E Zhu, B Li, L Jiang, X Zhang, ... arXiv preprint arXiv:2308.08155, 2023 | 417 | 2023 |
| 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 | 256* | 2021 |
| Datacomp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... Advances in Neural Information Processing Systems 36, 2024 | 234 | 2024 |
| Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias Y Yu*, Y Zhuang*, J Zhang*, Y Meng, A Ratner, R Krishna, J Shen, ... Advances in Neural Information Processing Systems 36, 2023 | 115 | 2023 |
| 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 | 107 | 2021 |
| A Survey on Programmatic Weak Supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022 | 89 | 2022 |
| Single-pass contrastive learning can work for both homophilic and heterophilic graph H Wang, J Zhang, Q Zhu, W Huang, K Kawaguchi, X Xiao Transactions on Machine Learning Research, 2023 | 72* | 2023 |
| A survey on deep graph generation: Methods and applications Y Zhu, Y Du, Y Wang, Y Xu, J Zhang, Q Liu, S Wu Learning on Graphs Conference, 47: 1-47: 21, 2022 | 70 | 2022 |
| SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality CY Hsieh*, J Zhang*, Z Ma, A Kembhavi, R Krishna arXiv preprint arXiv:2306.14610, 2023 | 65 | 2023 |
| Scibench: Evaluating college-level scientific problem-solving abilities of large language models X Wang, Z Hu, P Lu, Y Zhu, J Zhang, S Subramaniam, AR Loomba, ... arXiv preprint arXiv:2307.10635, 2023 | 60 | 2023 |
| 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 | 53 | 2020 |
| 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 | 44* | 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 | 39 | 2021 |
| TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations M Jiang, X Song, J Zhang, J Han Proceedings of the ACM Web Conference 2022, 925-934, 2022 | 37 | 2022 |
| 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 | 35 | 2020 |
| 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 | 33* | 2022 |
| Neural embedding propagation on heterogeneous networks C Yang*, J Zhang*, J Han 2019 IEEE International Conference on Data Mining (ICDM), 698-707, 2019 | 24 | 2019 |
| EcoAssistant: Using LLM Assistant More Affordably and Accurately J Zhang, R Krishna, AH Awadallah, C Wang arXiv preprint arXiv:2310.03046, 2023 | 23 | 2023 |
| EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records W Shi, R Xu, Y Zhuang, Y Yu, J Zhang, H Wu, Y Zhu, J Ho, C Yang, ... arXiv preprint arXiv:2401.07128, 2024 | 19* | 2024 |
| 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 | 18 | 2021 |
Yue YuCollege of Computing, Georgia Institute of TechnologyVerified email at gatech.edu
