Publication

(30) [NN]. Tanmoy Chowdhury, Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, and Liang Zhao. Deep Graph Representation Learning Influence Maximization with Accelerated Inference. Neural Networks, (impact factor: 6.0).

(29) [ICML WS 2024]. Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao, Haoyu Wang, Junxiang Wang, Haifeng Chen, and Jie Gao. RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection. ICML 2024 workshop on Geometry-grounded Representation Learning and Generative Modeling. paper

(28) [KDD 2024]. Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, and Haifeng Chen. POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), research track (acceptance rate: 20.0%), Barcelona, Spain, Aug 2024. paper supp slides poster

(27) [SDM 2024]. Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, and Liang Zhao. Non-Euclidean Spatial Graph Neural Network. In Proceedings of SIAM International Conference on Data Mining (SDM 2024), (acceptance rate: 29.2%), Houston, TX, USA, Apr 2024. paper

(26) [NeurIPS 2023]. Zheng Zhang, Junxiang Wang and Liang Zhao. Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First. In proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2023), (Acceptance Rate: 26.1%), New Orelans, USA, December 2023. paper code poster

(25) [ICML 2023]. Chen Ling, Junji Jiang, Junxiang Wang, My Thai, Lukas Xue, James Song, Meikang Qiu, and Liang Zhao. Deep Graph Representation Learning and Optimization for Influence Maximization. In proceedings of the International Conference on Machine Learning (ICML 2023), (acceptance rate: 27.9%), Honolulu, Hawaii, USA, July 2023. paper code

(24) [SDM 2023]. Guangji Bai, Johnny Torres, Junxiang Wang, Zhao Liang, Cristina Abad, and Carmen Vaca. Sign-Regularized Multi-Task Learning. in Proceedings of SIAM International Conference on Data Mining (SDM 2023), (acceptance rate: 27.4%), Minneapolis, Minn, USA, Apr 2023. paper code

(23) [TNNLS]. Junxiang Wang, Hongyi Li (first-coauthor), Zheng Chai, Yongchao Wang, Yue Cheng, and Liang Zhao. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. IEEE Transactions on Neural Networks and Learning Systems, (impact factor: 14.255)，2022. paper code

(22) [ICDM 2022]. Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. DeepAR: Deep Graph Representation Learning and Optimization for Analogical Reasoning. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2022), short paper (acceptance rate: 20%), Orlando, FL, USA, Nov 2022. paper

(21) [KDD 2022]. Chen Ling, Junji Jiang, Junxiang Wang and Liang Zhao. Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), research track (acceptance rate: 15.0%), Washington D.C, USA, Aug 2022. paper slides

(20) [Neurocomputing]. Junxiang Wang, Hongyi Li, and Liang Zhao. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Neurocomputing, (impact factor: 5.719), 2022. paper code The workshop version is in ICML 2021 Workshop on Beyond First-Order Methods in ML Systems. paper slides video

(19) [WWW 2022]. Junxiang Wang, Junji Jiang, and Liang Zhao. An Invertible Graph Diffusion Neural Network for Source Localization. 31st International World Wide Web Conference (WWW 2022), (acceptance rate: 17.7%), Lyon, FR, Apr 2022. paper code slides

(18) [PAKDD 2022]. Junxiang Wang and Liang Zhao. Convergence and Applications of ADMM on the Multi-convex Problems. 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022), (acceptance rate: 19.3%), Chengdu, China, May 2022. paper code slides

(17) [OPT 2021]. Hongyi Li, Junxiang Wang, Yongchao Wang, Yue Cheng, and Liang Zhao. Community-based Layerwise Distributed Training of Graph Convolutional Networks. NeurIPS 2021 Workshop on Optimization for Machine Learning (OPT 2021). paper poster

(16) [ICML WS 2020]. Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao. Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training. ICML 2020 Workshop on Beyond First-Order Methods in ML Systems. paper code video slides

(15) [RICO]. Junxiang Wang and Liang Zhao. Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems. Results in Control and Optimization, 2021. paper code

(14) [ICDM 2020]. Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao. Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2020), regular paper (acceptance rate: 9.8%), Sorrento, Italy, Nov 2020. paper code slides

(13) [OPT 2019]. Junxiang Wang and Liang Zhao. The Application of Multi-block ADMM on Isotonic Regression Problems. 11th Workshop on Optimization for Machine Learning (OPT 2019), co-located with NeurIPS 2019. paper code poster

(12) [KDD 2019]. Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. ADMM for Efficient Deep Learning with Global Convergence. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), Alaska, USA, Aug 2019. paper material code video slides poster summary Chinese media coverage

(11) [BigData 2018]. Junxiang Wang, Liang Zhao, and Yanfang Ye. Semi-supervised Multi-instance Interpretable Models for Flu Shot Adverse Event Detection. 2018 IEEE International Conference on Big Data (BigData 2018) (acceptance rate: 18.9%), Seattle, USA, Dec 2018. paper code slides

(10) [ICDM 2018]. Junxiang Wang, Yuyang Gao, Andreas Zufle, Jingyuan Yang, and Liang Zhao. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018. paper code data slides poster

(9) [J. Biomed. Semant]. Junxiang Wang, Liang Zhao, Yanfang Ye, and Yuji Zhang. Adverse event detection by integrating Twitter data and VAERS. Journal of Biomedical Semantics, (impact factor: 1.845), 2018. paper

(8) [WWW 2018]. Junxiang Wang and Liang Zhao. Multi-instance Domain Adaptation for Vaccine Adverse Event Detection. 27th International World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018. paper code slides

(7) [AAAI 2018]. Liang Zhao, Junxiang Wang, and Xiaojie Guo. Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. 4498-4505, New Orleans, US, Feb 2018. paper

(6) [Proc. IEEE]. Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu and Naren Ramakrishnan. “Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization”. Proceedings of the IEEE (impact factor: 9.237), vol. 105, no. 10, pp. 1953-1970, Oct. 2017. paper

(5) [Lett.Drug.Des.Discov]. Junxiang Wang, Weiming Yu, Zhibin Chen, Hengda Li, Zhenran Jiang. Predicting Drug-Target Interactions of Nuclear Receptors Based on Molecular Descriptors Information. Letters in Drug Design & Discovery 10 (10), 989-994, (impact factor: 1.099), 2013. paper

(4) [Pharmacogenomics]. Weiming Yu, Yan Yan, Qing Liu, Junxiang Wang and Zhenran Jiang. Predicting drug–target interaction networks of human diseases based on multiple feature information. Pharmacogenomics 14 (14), 1701-1707, 20, (impact factor: 2.638), 2013. paper

(3) [Curr. Bioinform]. Zhenran Jiang, Ran Tao, Lei Du, Weiming Yu and Junxiang Wang. Using Network-Based Approaches to Predict Ligands of Orphan Nuclear Receptors. Current Bioinformatics 7 (4), 411-414, (impact factor: 4.850), 2012. paper

(2) [Comb. Chem. High Throughput Screen]. Ran Tao, Zhenran Jiang, Weiming Yu and Junxiang Wang. Predicting Coupling Specificity of GPCRs Based on the Optimization of the Coupling Regions. Combinatorial chemistry & high throughput screening 15 (9), 770-774, (impact factor: 1.714), 2012. paper

(1) [Curr. Med. Chem]. Weiming Yu, Zhengyan Jiang, Junxiang Wang and Ran Tao. Using feature selection technique for drug-target interaction networks prediction. Current medicinal chemistry 18 (36), 5687-5693, (impact factor: 4.184), 2011. paper