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ORCIDYanqiao Zhu 0001
朱彦樵
Person information
- affiliation: University of California, Los Angeles, CA, USA
- affiliation (Master, 2022): Chinese Academy of Sciences, Institute of Automation, Beijing, China
- affiliation (Bachelor, 2019): Tongji University, Shanghai, China
- unicode name: 朱彦樵
Other persons with the same name
- Yanqiao Zhu 0002 — Peking University, Beijing, China
- Yanqiao Zhu 0003 — Shanghai Jiao Tong University, China
- Yanqiao Zhu 0004 — Northeastern University, College of Software, Shenyang, China
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2020 – today
- 2025
[c30]Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang:
Efficient Evolutionary Search Over Chemical Space with Large Language Models. ICLR 2025- [i43]Jingru Gan, Peichen Zhong, Yuanqi Du, Yanqiao Zhu, Chenru Duan, Haorui Wang, Carla P. Gomes, Kristin A. Persson, Daniel Schwalbe-Koda, Wei Wang:
Large Language Models Are Innate Crystal Structure Generators. CoRR abs/2502.20933 (2025) - [i42]Junkai Zhang, Jingru Gan, Xiaoxuan Wang, Zian Jia, Changquan Gu, Jianpeng Chen, Yanqiao Zhu, Mingyu Derek Ma, Dawei Zhou, Ling Li, Wei Wang:
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science. CoRR abs/2510.12171 (2025) - 2024
- [j8]Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu
:
Molecular Contrastive Pretraining with Collaborative Featurizations. J. Chem. Inf. Model. 64(4): 1112-1122 (2024) - [j7]Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang:
Bi-Level Graph Structure Learning for Next POI Recommendation. IEEE Trans. Knowl. Data Eng. 36(11): 5695-5708 (2024) - [c29]Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce C. Ho, Chao Zhang, Carl Yang:
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers. EMNLP 2024: 22234-22254 - [c28]Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang:
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. ICLR 2024 - [c27]Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang:
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models. ICML 2024 - [i41]Hengchuang Yin, Zhonghui Gu, Fanhao Wang, Yiparemu Abuduhaibaier, Yanqiao Zhu, Xinming Tu, Xian-Sheng Hua, Xiao Luo, Yizhou Sun:
An Evaluation of Large Language Models in Bioinformatics Research. CoRR abs/2402.13714 (2024) - [i40]Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D. Wang, Joyce C. Ho, Chao Zhang, Carl Yang:
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers. CoRR abs/2404.18443 (2024) - [i39]Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Streith-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang:
Efficient Evolutionary Search Over Chemical Space with Large Language Models. CoRR abs/2406.16976 (2024) - [i38]Chenchen Ye, Ziniu Hu, Yihe Deng, Zijie Huang, Mingyu Derek Ma, Yanqiao Zhu, Wei Wang:
MIRAI: Evaluating LLM Agents for Event Forecasting. CoRR abs/2407.01231 (2024) - [i37]Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang:
Bi-Level Graph Structure Learning for Next POI Recommendation. CoRR abs/2411.01169 (2024) - 2023
- [j6]Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu:
Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining. ACM Trans. Intell. Syst. Technol. 14(5): 82:1-82:21 (2023) - [j5]Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang:
Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation. IEEE Trans. Knowl. Data Eng. 35(9): 9154-9167 (2023) - [j4]Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang:
BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks. IEEE Trans. Medical Imaging 42(2): 493-506 (2023) - [c26]Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C. Ho, Chao Zhang, Carl Yang:
Neighborhood-Regularized Self-Training for Learning with Few Labels. AAAI 2023: 10611-10619 - [c25]Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li:
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange. CIKM 2023: 3134-3143 - [c24]Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li:
A Systematic Survey of Chemical Pre-trained Models. IJCAI 2023: 6787-6795 - [c23]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis. ISBI 2023: 1-5 - [c22]Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan:
The Second Learning on Graphs Conference: Preface. LoG 2023: i-xix - [c21]Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang:
Uncovering Neural Scaling Laws in Molecular Representation Learning. NeurIPS 2023 - [c20]Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla Pedro Gomes:
M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. NeurIPS 2023 - [c19]Zhixun Li, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu:
GSLB: The Graph Structure Learning Benchmark. NeurIPS 2023 - [c18]Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang:
Code Recommendation for Open Source Software Developers. WWW 2023: 1324-1333 - [i36]Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C. Ho, Chao Zhang, Carl Yang:
Neighborhood-Regularized Self-Training for Learning with Few Labels. CoRR abs/2301.03726 (2023) - [i35]Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla P. Gomes:
M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. CoRR abs/2307.05378 (2023) - [i34]Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang:
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models. CoRR abs/2307.10635 (2023) - [i33]Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang:
Uncovering Neural Scaling Laws in Molecular Representation Learning. CoRR abs/2309.15123 (2023) - [i32]Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang:
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. CoRR abs/2310.00115 (2023) - [i31]Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu:
GSLB: The Graph Structure Learning Benchmark. CoRR abs/2310.05174 (2023) - 2022
- [c17]Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang:
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks (Extended Abstract). IEEE Big Data 2022: 4968-4969 - [c16]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). IEEE Big Data 2022: 4995-4996 - [c15]Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang:
Deep Contrastive Multiview Network Embedding. CIKM 2022: 4692-4696 - [c14]Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang:
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis. EMBC 2022: 272-276 - [c13]Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang:
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning. KDD 2022: 4743-4751 - [c12]Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu:
A Survey on Deep Graph Generation: Methods and Applications. LoG 2022: 47 - [c11]Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov:
The First Learning on Graphs Conference: Preface. LoG 2022: i-xxiii - [c10]Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang:
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. MICCAI (8) 2022: 375-385 - [c9]Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu:
Structure-Enhanced Heterogeneous Graph Contrastive Learning. SDM 2022: 82-90 - [i30]Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li:
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications. CoRR abs/2202.07893 (2022) - [i29]Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu:
A Survey on Deep Graph Generation: Methods and Applications. CoRR abs/2203.06714 (2022) - [i28]Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang:
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks. CoRR abs/2204.07054 (2022) - [i27]Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang:
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning. CoRR abs/2206.04486 (2022) - [i26]Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang:
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. CoRR abs/2207.00813 (2022) - [i25]Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu:
Improving Molecular Pretraining with Complementary Featurizations. CoRR abs/2209.15101 (2022) - [i24]Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang:
Code Recommendation for Open Source Software Developers. CoRR abs/2210.08332 (2022) - [i23]Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, Stan Z. Li:
A Systematic Survey of Molecular Pre-trained Models. CoRR abs/2210.16484 (2022) - [i22]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks. CoRR abs/2211.00261 (2022) - [i21]Yichen Xu, Yanqiao Zhu:
A Survey on Pretrained Language Models for Neural Code Intelligence. CoRR abs/2212.10079 (2022) - 2021
- [j3]Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan:
GraphAIR: Graph representation learning with neighborhood aggregation and interaction. Pattern Recognit. 112: 107745 (2021) - [c8]Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu:
Deep Active Learning for Text Classification with Diverse Interpretations. CIKM 2021: 3263-3267 - [c7]Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu:
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction. CIKM 2021: 3553-3557 - [c6]Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang:
Mining Latent Structures for Multimedia Recommendation. ACM Multimedia 2021: 3872-3880 - [c5]Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu:
An Empirical Study of Graph Contrastive Learning. NeurIPS Datasets and Benchmarks 2021 - [c4]Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang:
Graph Contrastive Learning with Adaptive Augmentation. WWW 2021: 2069-2080 - [i20]Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang:
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction. CoRR abs/2101.03654 (2021) - [i19]Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang:
Deep Graph Structure Learning for Robust Representations: A Survey. CoRR abs/2103.03036 (2021) - [i18]Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang:
Mining Latent Structures for Multimedia Recommendation. CoRR abs/2104.09036 (2021) - [i17]Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li:
Graph Symbiosis Learning. CoRR abs/2106.05455 (2021) - [i16]Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang:
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis. CoRR abs/2107.03220 (2021) - [i15]Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang:
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis. CoRR abs/2107.05097 (2021) - [i14]Mengqi Zhang, Yanqiao Zhu, Shu Wu, Liang Wang:
Deep Contrastive Learning for Multi-View Network Embedding. CoRR abs/2108.08296 (2021) - [i13]Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu:
Deep Active Learning for Text Classification with Diverse Interpretations. CoRR abs/2108.10687 (2021) - [i12]Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu:
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning. CoRR abs/2108.13886 (2021) - [i11]Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu:
An Empirical Study of Graph Contrastive Learning. CoRR abs/2109.01116 (2021) - [i10]Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang:
Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation. CoRR abs/2111.00678 (2021) - 2020
- [c3]Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan:
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation. SIGIR 2020: 1921-1924 - [i9]Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan:
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation. CoRR abs/2005.02844 (2020) - [i8]Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang:
Deep Graph Contrastive Representation Learning. CoRR abs/2006.04131 (2020) - [i7]Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, Liang Wang:
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning. CoRR abs/2009.01674 (2020) - [i6]Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang:
Graph Contrastive Learning with Adaptive Augmentation. CoRR abs/2010.14945 (2020) - [i5]Yanqiao Zhu, Weizhi Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang:
Deep Active Graph Representation Learning. CoRR abs/2010.16091 (2020)
2010 – 2019
- 2019
- [j2]Yanqiao Zhu, Kai Yang:
Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access 7: 63195-63203 (2019) - [c2]Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan:
Session-Based Recommendation with Graph Neural Networks. AAAI 2019: 346-353 - [c1]Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan:
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. IJCAI 2019: 4532-4539 - [i4]Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan:
Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. CoRR abs/1902.06667 (2019) - [i3]Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan:
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction. CoRR abs/1911.01731 (2019) - 2018
- [j1]Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang:
Active Learning for Wireless IoT Intrusion Detection. IEEE Wirel. Commun. 25(6): 19-25 (2018) - [i2]Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang:
Active Learning for Wireless IoT Intrusion Detection. CoRR abs/1808.01412 (2018) - [i1]Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan:
Session-based Recommendation with Graph Neural Networks. CoRR abs/1811.00855 (2018)
Coauthor Index
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