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Quanquan Gu

Active 2008–2026

Also published as
Quanquan, Gu
364
Papers
18,898
Citations
75
h-index
255
i10-index

Citations

Citations per year for Quanquan Gu1967: 1 citations1988: 1 citations1989: 1 citations1999: 1 citations2001: 2 citations2007: 1 citations2008: 6 citations2009: 6 citations2010: 21 citations2011: 40 citations2012: 65 citations2013: 94 citations2014: 178 citations2015: 206 citations2016: 266 citations2017: 359 citations2018: 431 citations2019: 637 citations2020: 922 citations2021: 1,244 citations2022: 997 citations2023: 890 citations2024: 1,320 citations2025: 1,672 citations2026: 547 citations2027: 1 citations1968–1987: no citations, so these years are not shown1990–1998: no citations, so these years are not shown2000: no citations, so this year is not shown2002–2006: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 2,428 citing papers, 28.8% of this breakdownUnited States: 2,192 citing papers, 26% of this breakdownUnited Kingdom: 380 citing papers, 4.5% of this breakdownAustralia: 304 citing papers, 3.6% of this breakdownHong Kong: 264 citing papers, 3.1% of this breakdownCanada: 245 citing papers, 2.9% of this breakdownGermany: 216 citing papers, 2.6% of this breakdownFrance: 201 citing papers, 2.4% of this breakdownSingapore: 199 citing papers, 2.4% of this breakdownIndia: 186 citing papers, 2.2% of this breakdownJapan: 167 citing papers, 2% of this breakdownItaly: 134 citing papers, 1.6% of this breakdown
0%28.8%Other 17.9%

Institutions

Fields

  • Computer Science66.3%
  • Engineering6.7%
  • Medicine5.3%
  • Decision Sciences4.7%
  • Mathematics3.6%
  • Physics and Astronomy3.5%
  • Other9.9%

Topics

  • Topic Modeling4.7%
  • Advanced Graph Neural Networks4.1%
  • Recommender Systems and Techniques3.7%
  • Stochastic Gradient Optimization Techniques3.6%
  • Adversarial Robustness in Machine Learning3.4%
  • Face and Expression Recognition2.4%
  • Other78.1%

Coauthors

All papers

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  1. TrustLLM: Trustworthiness in Large Language Models

    Authors: Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao - arXiv (Cornell University), CoRR 2024 cited by 410

  2. Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

    Authors: Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu - ICML 2024 cited by 607

  3. Self-Play Preference Optimization for Language Model Alignment

    Authors: Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu - ICLR 2025 cited by 266

  4. Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

    Authors: Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao - ACM Computing Surveys, ACM Comput. Surv. 2025 cited by 157

  5. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

    Authors: Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu - arXiv (Cornell University), CoRR 2023 cited by 102

  6. Personalized entity recommendation: a heterogeneous information network approach

    Authors: Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han - international conference on Web search and data mining, WSDM 2014 cited by 745

  7. LLaVA-Critic: Learning to Evaluate Multimodal Models

    Authors: Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li - IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 cited by 87

  8. Towards Understanding the Spectral Bias of Deep Learning

    Authors: Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu - Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021 cited by 153

  9. Diffusion Language Models Are Versatile Protein Learners

    Authors: Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu - ICML 2024 cited by 136

  10. How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?

    Authors: Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett - ICLR 2024 cited by 102

  11. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

    Authors: Estee Y. Cramer, Evan L Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie House, Yuxin Huang, Dasuni Jayawardena, Abdul Hannan Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer J. Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover George, Jeffrey C. Cegan, Ian Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon J. Lafferty, Igor Linkov, Michael L. Mayo, Matthew Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang‐James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina Corsetti, T. M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Gibson, Rose Yu, Liyao Gao, Yi-An Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, J. Lega, Steve McConnell, VP Nagraj, Stephanie Guertin, Christopher Hulme-Lowe, Stephen Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi‐Jun Hong, Stanley Kong, Axel van de Walle and 195 more - National Academy of Sciences, Proceedings of the National Academy of Sciences 2022 cited by 320

  12. Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance

    Authors: Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu - ICML 2025 cited by 50

  13. DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

    Authors: Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu - ICML 2023 cited by 137

  14. Is neuron coverage a meaningful measure for testing deep neural networks?

    Authors: Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim - Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2020 cited by 151

  15. Towards Understanding Mixture of Experts in Deep Learning

    Authors: Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li - arXiv (Cornell University), CoRR 2022 cited by 66

  16. Generalized Fisher Score for Feature Selection

    Authors: Quanquan Gu, Zhenhui Li, Jiawei Han - http://faculty.ist.psu.edu/jessieli/Publications/uai11.pdf 2011 cited by 461

  17. DPLM-2: A Multimodal Diffusion Protein Language Model

    Authors: Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu - ICLR 2025 cited by 75

  18. Enhancing Large Vision Language Models with Self-Training on Image Comprehension

    Authors: Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, Quanquan Gu, James Y. Zou, Kai-Wei Chang, Wei Wang - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 93

  19. Reinforcement Learning from Human Feedback with Active Queries

    Authors: Kaixuan Ji, Jiafan He, Quanquan Gu - Trans. Mach. Learn. Res. 2025 cited by 37

  20. Neural Thompson Sampling

    Authors: Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu - ICLR 2021 cited by 159

  21. Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning

    Authors: Jiasheng Ye, Zaixiang Zheng, Yu Bao, Lihua Qian, Quanquan Gu - arXiv (Cornell University), CoRR 2023 cited by 36

  22. Improving Adversarial Robustness Requires Revisiting Misclassified Examples

    Authors: Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu - International Conference on Learning Representations, ICLR 2020 cited by 851

  23. Gradient descent optimizes over-parameterized deep ReLU networks

    Authors: Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu - Machine Learning, Mach. Learn. 2019 cited by 226

  24. Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation

    Authors: Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 94