Publications (selected)

Nelson Ma, Junyu Xuan, Guangquan Zhang, Jie Lu (2025). Discovering Mixture Skills for Unsupervised Reinforcement Learning. AAAI.
Wei Duan, Jie Lu, Junyu Xuan (2025). Bayesian ego-graph inference for networked multi-agent reinforcement learning. NeurIPS.
Zhe Chen, Junyu Xuan (2025). Bayesian adversarial inverse reinforcement learning for UAV obstacle avoidance in confined spaces. ICAUS.
Mengjing Wu, Junyu Xuan, Jie Lu (2025). Bridging the gap between variational inference and stochastic gradient MCMC in function space. ICLR.
Mengjing Wu, Junyu Xuan, Jie Lu (2025). Functional stochastic gradient MCMC for Bayesian neural networks. AISTATS.
Samuel Pradhan, Guanjin Wang, Junyu Xuan, Penghao Wang, Chengdao Li, Jie Lu (2025). A multimodal deep learning end-to-end model for improving barley genotype-to-phenotype prediction using heterogeneous data. IEEE CAI.
Kiyoshige Garces, Gloria Milena Fernandez-Nieto, Mladen Rakovic, Xinju Li, Tongguang Li, Linxuan Zhao, Dragan Gasevic, Junyu Xuan, Hua Zuo (2025). Transfer reinforcement learning for self-regulated learning support: an evaluation using successor features. AIED.
Guanjin Wang, Junyu Xuan, Penghao Wang, Chengdao Li, Jie Lu (2025). LSTM autoencoder-based deep neural networks for barley genotype-to-phenotype prediction. AJCAI.
Mengjing Wu, Junyu Xuan, Jie Lu (2024). Functional Wasserstein bridge inference for Bayesian deep learning. UAI.
Junyu Xuan, Mengjing Wu, Zihe Liu, Jie Lu (2024). Functional Wasserstein variational policy optimization. UAI.
Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhang (2024). A behavior-aware approach for deep reinforcement learning in non-stationary environments without known change points. IJCAI.
Wei Duan, Jie Lu, Junyu Xuan (2024). Group-aware coordination graph for multi-agent reinforcement learning. IJCAI.
Mengjing Wu, Junyu Xuan, Jie Lu (2023). Indirect functional Bayesian neural networks. AABI.
Kiyoshige Garces, Junyu Xuan, Hua Zuo (2023). Transformed successor teatures for transfer reinforcement learning. AJCAI.
Chris Shen, Junyu Xuan, Jie Liang (2023). Mitigating intrinsic named entity hallucinations of abstractive text summarization. EMNLP (Findings).
Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu (2022). Learning from the dark: Boosting graph convolutional neural networks with diverse negative samples. AAAI.
Zheng Ma, Junyu Xuan, Yuguang Wang, Ming Li, Pietro Lio (2020). Path integral based convolution and pooling for graph neural networks. NIPS.
Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang (2019). One-stage deep instrumental variable method for causal inference from observational data. ICDM.
Qian Liu, Heyan Huang, Guangquan Zhang, Yang Gao, Junyu Xuan, Jie Lu (2019). Semantic structure-based word embedding by incorporating concept convergence and word divergence. AAAI.
Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Xu (2015). Infinite author topic model through mixed Gamma negative binomial processes. ICDM.
Kiyoshige Garces, Junyu Xuan, Hua Zuo (2025). Adaptive successor features composition for transfer reinforcement learning. IEEE Transactions on Artificial Intelligence.
Haotian Xu, Junyu Xuan, Guangquan Zhang, Jie Lu (2025). Twin trust region policy optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhang (2025). Learning latent and changing dynamics in true non-stationary environments. IEEE Transactions on Knowledge and Data Engineering.
Nelson Ma, Junyu Xuan, Guangquan Zhang Jie Lu (2025). Global-Local decomposition of contextual representations in meta-reinforcement learning. IEEE Transactions on Cybernetics.
Wei Duan, Jie Lu, Junyu Xuan (2024). Inferring latent temporal sparse coordination graph for multi-agent reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems.
Wei Duan, Jie Lu, Yuguang Wang Junyu Xuan (2024). Layer-diverse negative sampling for graph neural networks. Transactions on Machine Learning Research.
Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhang (2024). Robust deep reinforcement learning in obscure non-stationary environments with unknown boundaries. IEEE Transactions on Cybernetics.
Fujin Zhu, Jie Lu, Adi Lin, Junyu Xuan, Guangquan Zhang (2023). TauNet: Direct learning for heterogeneous treatment effect estimation using deep neural networks. IEEE Transactions on Knowledge and Data Engineering.
Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu (2023). Graph convolutional neural networks with diverse negative samples via decomposed determinant point processes. IEEE Transactions on Neural Networks and Learning Systems.
Anjin Liu, Jie Lu, Yiliao Song, Junyu Xuan, Guangquan Zhang (2023). Concept drift detection delay index. IEEE Transactions on Knowledge and Data Engineering.
Nan Xia, Hang Yu, Yin Wang, Junyu Xuan, Xiangfeng Luo (2022). DAFS: a domain aware few shot generative model for event detection. Machine Learning.
Zheng Ma, Junyu Xuan, Yuguang Wang, Ming Li, Pietro Lio (2021). Path integral based convolution and pooling for graph neural networks. Journal of Statistical Mechanics: Theory and Experiment.
Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang (2021). Open set domain adaptation: Theoretical bound and algorithm. IEEE Transactions on Neural Network and Learning Systems.
Qian Liu, Heyan Huang, Junyu Xuan, Guangquan Zhang, Yang Gao, Jie Lu (2021). A fuzzy word similarity measure for selecting top-k similar words in query expansion. IEEE Transactions on Fuzzy Systems.
Junyu Xuan, Jie Lu, Guangquan Zhang (2020). Bayesian nonparametric unsupervised concept drift detection for data stream mining. ACM Transactions on Intelligent Systems and Technology.
Adi Lin, Jie Lu, Junyu Xuan, Guangquan Zhang (2020). A causal Dirichlet mixture model for causal inference from observational data. ACM Transactions on Intelligent Systems and Technology.
Junyu Xuan, Jie Lu, Guangquan Zhang (2019). Cooperative hierarchical Dirichlet processes: Superposition vs. maximization. Artificial Intelligence.
Junyu Xuan, Jie Lu, Guangquan Zhang (2019). A survey on Bayesian nonparametric learning. ACM Computing Surveys.
Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Xu, Xiangfeng Luo (2018). Doubly nonparametric sparse nonnegative matrix factorization based on dependent Indian buffet processes. IEEE Transactions on Neural Network and Learning Systems.
Junyu Xuan, Jie Lu, Guangquan Zhang, Xiangfeng Luo (2018). Structural property-aware multilayer network embedding for latent factor analysis. Pattern Recognition.
Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Xu, Xiangfeng Luo (2017). A Bayesian nonparametric model for multi-label learning. Machine Learning.
Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Xu, Xiangfeng Luo (2017). Bayesian nonparametric relational topic model through dependent Gamma processes. IEEE Transactions on Knowledge and Data Engineering.
Junyu Xuan, Xiangfeng Luo, Guangquan Zhang, Jie Lu, Zheng Xu (2016). Uncertainty analysis for the keyword system of web events. IEEE Transactions on Systems Man and Cybernetics: Systems.
Xiangfeng Luo, Junyu Xuan, Jie Lu, Guangquan Zhang (2016). Measuring the semantic uncertainty of news events for evolution potential estimation. ACM Transactions on Information Systems.
Junyu Xuan, Jie Lu, Guangquan Zhang, Xiangfeng Luo (2015). Topic model for graph mining. IEEE Transactions on Cybernetics.