Han, Jiawei

Mining frequent patterns without candidate generation

Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record

Data Mining Algorithms and ApplicationsAdvanced Database Systems and Queries

Cited by 6,402

Co-author Relationship Prediction in Heterogeneous Bibliographic Networks

Sun, Y., Barber, R., Gupta, M., Aggarwal, C. C., & Han, J. (2011) · 2011 International Conference on Advances in Social Networks Analysis and Mining

Complex Network Analysis Techniquesscientometrics and bibliometrics research

Cited by 445

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Ouyang, S., Yan, J., Hsu, I.-H., Chen, Y., Jiang, K., Wang, Z., Han, R., Le, L. T., Daruki, S., Tang, X., et al. (2025) · arXiv

AI-based Problem Solving and PlanningDomain Adaptation and Few-Shot Learning

Cited by 295

TrustLLM: Trustworthiness in Large Language Models

Huang, Y., Sun, L., Wang, H., Wu, S., Zhang, Q., Li, Y., Gao, C., Huang, Y., Lyu, W., Zhang, Y., et al. (2024) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 256

Large Language Models Can Self-Improve

Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 230

Large Language Models Can Self-Improve

Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 230

Generation-Augmented Retrieval for Open-Domain Question Answering

Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 146

Constrained frequent pattern mining: a pattern-growth view

Pei, J., & Han, J. (2002) · ACM SIGKDD Explorations Newsletter

Data Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

Cited by 133

Towards a Unified Multi-Dimensional Evaluator for Text Generation

Zhong, M., Liu, Y., Yin, D., Mao, Y., Jiao, Y., Liu, P., Zhu, C., Ji, H., & Han, J. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 117

An exploration of discussion threads in social news sites: a case study of the Reddit community

Weninger, T., Zhu, X. A., & Han, J. (2013) · Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 110

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Liu, L., Ren, X., Shang, J., Gu, X., Peng, J., & Han, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingAdvanced Neural Network Applications

Cited by 45

Generation-Augmented Retrieval for Open-domain Question Answering

Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2020) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 31

Don't Make Your LLM an Evaluation Benchmark Cheater

Zhou, K., Zhu, Y., Chen, Z., Chen, W., Zhao, W. X., Chen, X., Lin, Y., Wen, J.-R., & Han, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 25

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Jin, B., Zeng, H., Yue, Z., Yoon, J., Arik, S., Wang, D., Zamani, H., & Han, J. (2025) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 13

Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

Ge, S., Zhang, Y., Liu, L., Zhang, M., Han, J., & Gao, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

TriSum: Learning summarization ability from large language models with structured rationale

Jiang, P., Xiao, C., Wang, Z., Bhatia, P., Sun, J., & Han, J. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingAdvanced Text Analysis Techniques

Cited by 11

Structured Chemistry Reasoning with Large Language Models

Ouyang, S., Zhang, Z., Yan, B., Liu, X., Choi, Y., Han, J., & Qin, L. (2023) · arXiv

Machine Learning in Materials ScienceMultimodal Machine Learning Applications

Cited by 9

Towards a Unified Multi-Dimensional Evaluator for Text Generation

Zhong, M., Liu, Y., Yin, D., Mao, Y., Jiao, Y., Liu, P., Zhu, C., Ji, H., & Han, J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Language Models As Semantic Indexers

Jin, B., Zeng, H., Wang, G., Chen, X., Wei, T., Li, R., Wang, Z., Li, Z., Li, Y., Lu, H., et al. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 2

Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning

Wu, J., Zhong, X., Sun, J., Li, B., Jin, B., Han, J., & Zeng, Q. (2025) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0