I obtained my PhD degree in the Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST), 2019. My supervisors are Prof. Lionel M. Ni and Prof. James T. Kwok. Before that, I obtained my Bachelor degree in School of Computer Science and Technology, Shandong University, 2014. Now I am working at Baidu Research.
Recently, I am mainly working on few-shot learning (FSL) and meta-learning methods and their application to diverse application scenarios:
Our team maintains PaddleFSL, a Python toolkit for FSL built upon PaddlePaddle. PaddleFSL provides various FSL solutions which are applicable to diverse applications. Please see the paper for details.
* indicates equal contribution, † indicates corresponding author
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
Shiguang Wu, Yaqing Wang†, Quanming Yao
International Joint Conference on Artificial Intelligence (IJCAI), 2024
ColdU: Cold-start Recommendation with User-specific Modulation
Daxiang Dong, Shiguang Wu, Yaqing Wang, Jingbo Zhou, Haifeng Wang
IEEE Conference on Artificial Intelligence (CAI), 2024
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Quanming Yao, Zhenqian Shen, Yaqing Wang†, Dejing Dou
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Accurate and Interpretable Drug-drug Interaction Prediction Enabled by Knowledge Subgraph Learning
Yaqing Wang*, Zaifei Yang*, Quanming Yao
Communications Medicine (Commun. Med., Nature Portfolio), 2024
[Official Link]
Feynman: Federated Advertising for Ecosystems-Oriented Mobile Apps Recommendation
Jiang Bian, Jizhou Huang, Shilei Ji, Yuan Liao, Xuhong Li, Qingzhong Wang, Jingbo Zhou, Yaqing Wang, Dejing Dou, Haoyi Xiong
IEEE Transactions on Service Computing (TSC), 2023
[Official Link]
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023
[Official Link]
A Novel Tensor Learning Model for Joint Relational Triplet Extraction
Zhen Wang, Hongyi Nie, Wei Zheng, Yaqing Wang, Xuelong Li
IEEE Transactions on Cybernetics (TCYB), 2023
[Official Link]
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Shiguang Wu, Yaqing Wang†, Qinghe Jing, Daxiang Dong, Dejing Dou, Quanming Yao
The Web Conference (TheWebConf, previous WWW), 2023
[Official Link]
[Code]
Simplified Graph Learning for Inductive Short Text Classification
Kexin Zheng*, Yaqing Wang*†, Quanming Yao, Dejing Dou
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
[Official Link]
[Paper]
[Code]
Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, Dejing Dou
Neural Information Processing Systems (NeurIPS), 2022
[Official Link]
[Paper]
[Code]
Efficient Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang†, Bo Han, James T. Kwok
Journal of Machine Learning Research (JMLR), 2022
[Official Link]
[Paper]
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning
Yaqing Wang*, Xin Tian*, Haoyi Xiong, Yueyang Li, Zeyu Chen, Sheng Guo, Dejing Dou
Findings of the ACL: NAACL (NAACL Findings), 2022
[Official Link]
[Paper]
[Code]
Recognizing Medical Search Query Intent by Few-shot Learning
Yaqing Wang*, Song Wang*, Yanyan Li, Dejing Dou
International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Long Paper, 2022
[Official Link]
[Paper]
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, Dejing Dou
Machine Learning (MLJ), 2022 (Also selected for the Journal Track of AAAI 2023)
[Official Link]
[Paper]
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou
Machine Learning (MLJ), 2022
[Official Link]
[Paper]
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Yaqing Wang*, Abulikemu Abuduweili*, Quanming Yao, Dejing Dou
Neural Information Processing Systems (NeurIPS), Spotlight (< 3%), 2021
[Official Link]
[Paper]
[Code]
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Yaqing Wang, Song Wang, Quanming Yao, Dejing Dou
Conference on Empirical Methods in Natural Language Processing (EMNLP), Long Paper, 2021
[Official Link]
[Paper]
[Code]
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning
Yaqing Wang, Quanming Yao, James T. Kwok
The Web Conference (TheWebConf, previous WWW), 2021
[Official Link]
[Paper]
Generalized Convolutional Sparse Coding with Unknown Noise
Yaqing Wang, James T. Kwok, Lionel M. Ni
IEEE Transactions on Image Processing (TIP), 2020
[Official Link]
[Paper]
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni
ACM Computing Surveys (CSUR), 2020
[Official Link]
[Paper]
[FSL Resources]
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni
International Conference on Machine Learning (ICML), 2018
[Official Link]
[Paper]
[Code]
Scalable Online Convolutional Sparse Coding
Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni
IEEE Transactions on Image Processing (TIP), 2018
[Official Link]
[Paper]
[Code]
Zero-Shot Learning with a Partial Set of Observed Attributes
Yaqing Wang, James T. Kwok, Quanming Yao, Lionel M. Ni
International Joint Conference on Neural Networks (IJCNN), 2017
[Official Link]
[Paper]
We always have positions for highly motivated and talented interns. Please don't hesitate to contact me with your CV if you are interested.
Interns who worked with me for 6 months+:
