Utilizing Partial Policies for Identifying Equivalence of Behavioral Models. - Yifeng Zeng; Prashant Doshi; Yinghui Pan; Hua Mao 001; Muthukumaran Chandrasekaran; Jian Luo (AAAI, 2011)

Dynamic Ordering-Based Search Algorithm for Markov Blanket Discovery. - Yifeng Zeng; Xian He; Yanping Xiang; Hua Mao 001 (PAKDD (2), 2011)

Learning Probabilistic Automata for Model Checking. - Hua Mao 001; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (QEST, 2011)

Improved use of partial policies for identifying behavioral equivalence. - Yifeng Zeng; Hua Mao 001; Yinghui Pan; Jian Luo (AAMAS, 2012)

An Optimization Approach to Believable Behavior in Computer Games. - Yifeng Zeng; Hua Mao 001; Fan Yang 010; Jian Luo (ADMI, 2012)

Learning Communication in Interactive Dynamic Influence Diagrams. - Yifeng Zeng; Hua Mao 001; Prashant Doshi; Yinghui Pan; Jian Luo (IAT, 2012)

Learning Markov Models for Stationary System Behaviors. - Yingke Chen; Hua Mao 001; Manfred Jaeger; Thomas Dyhre Nielsen; Kim Guldstrand Larsen; Brian Nielsen (NASA Formal Methods, 2012)

Learning Markov Decision Processes for Model Checking - Hua Mao 001; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (QFM, 2012)

Learning and Model-Checking Networks of I/O Automata. - Hua Mao 001; Manfred Jaeger (ACML, 2012)

Learning Agents' Relations in Interactive Multiagent Dynamic Influence Diagrams. - Yinghui Pan; Yifeng Zeng; Hua Mao 001 (ADMI, 2014)

Diversifying the Storytelling Using Bayesian Networks. - Wenyun Wu; Biyang Ma; Shaoxin Zhang; Yifeng Zeng; Hua Mao 001 (ADMI, 2014)

Continuity Properties of Distances for Markov Processes. - Manfred Jaeger; Hua Mao 001; Kim Guldstrand Larsen; Radu Mardare (QEST, 2014)

Manifold dimension reduction based clustering for multi-objective evolutionary algorithm. - Yanan Sun 001; Gary G. Yen; Hua Mao 001; Zhang Yi 001 (CEC, 2016)

Symmetric low-rank representation for subspace clustering. - Jie Chen 065; Haixian Zhang; Hua Mao 001; Yongsheng Sang; Zhang Yi 001 (Neurocomputing, 2016)

Approximating behavioral equivalence for scaling solutions of I-DIDs. - Yifeng Zeng; Prashant Doshi; Yingke Chen; Yinghui Pan; Hua Mao 001; Muthukumaran Chandrasekaran (Knowl. Inf. Syst., 2016)

Learning deterministic probabilistic automata from a model checking perspective. - Hua Mao 001; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (Mach. Learn., 2016)

Learning a good representation with unsymmetrical auto-encoder. - Yanan Sun 001; Hua Mao 001; Quan Guo; Zhang Yi 001 (Neural Comput. Appl., 2016)

Cell tracking using deep neural networks with multi-task learning. - Tao He 016; Hua Mao 001; Jixiang Guo; Zhang Yi 001 (Image Vis. Comput., 2017)

Subspace clustering using a symmetric low-rank representation. - Jie Chen 065; Hua Mao 001; Yongsheng Sang; Zhang Yi 001 (Knowl. Based Syst., 2017)

Protein secondary structure prediction by using deep learning method. - Yangxu Wang; Hua Mao 001; Zhang Yi 001 (Knowl. Based Syst., 2017)

Cell mitosis detection using deep neural networks. - Yao Zhou 002; Hua Mao 001; Zhang Yi 001 (Knowl. Based Syst., 2017)

Moving object recognition using multi-view three-dimensional convolutional neural networks. - Tao He 016; Hua Mao 001; Zhang Yi 001 (Neural Comput. Appl., 2017)

Explicit guiding auto-encoders for learning meaningful representation. - Yanan Sun 001; Hua Mao 001; Yongsheng Sang; Zhang Yi 001 (Neural Comput. Appl., 2017)

Parameter as a Switch Between Dynamical States of a Network in Population Decoding. - Jiali Yu; Hua Mao 001; Zhang Yi 001 (IEEE Trans. Neural Networks Learn. Syst., 2017)

Symmetric low-rank preserving projections for subspace learning. - Jie Chen 065; Hua Mao 001; Haixian Zhang; Zhang Yi 001 (Neurocomputing, 2018)

Audio classification using attention-augmented convolutional neural network. - Yu Wu; Hua Mao 001; Zhang Yi 001 (Knowl. Based Syst., 2018)

Spectrogram based multi-task audio classification. - Yuni Zeng; Hua Mao 001; Dezhong Peng; Zhang Yi 001 (Multim. Tools Appl., 2019)

Stem cell motion-tracking by using deep neural networks with multi-output. - Yangxu Wang; Hua Mao 001; Zhang Yi 001 (Neural Comput. Appl., 2019)

Efficient algorithms for collecting the statistics of large-scale IP address data. - Jie Chen 065; Hua Mao 001 (CoRR, 2021)

Low-rank representation with adaptive dictionary learning for subspace clustering. - Jie Chen 065; Hua Mao 001; Zhu Wang 007; Xinpei Zhang (Knowl. Based Syst., 2021)

Multiview Subspace Clustering Using Low-Rank Representation. - Jie Chen 065; Shengxiang Yang; Hua Mao 001; Conor Fahy (IEEE Trans. Cybern., 2022)

Subtraction Gates: Another Way to Learn Long-Term Dependencies in Recurrent Neural Networks. - Tao He 016; Hua Mao 001; Zhang Yi 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Deep Multiview Clustering by Contrasting Cluster Assignments. - Jie Chen 065; Hua Mao 001; Wai Lok Woo; Xi Peng 001 (ICCV, 2023)

Cross-View Graph Consistency Learning for Invariant Graph Representations. - Jie Chen 065; Zhiming Li; Hua Mao 001; Wai Lok Woo; Xi Peng 001 (CoRR, 2023)

Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning. - Jacob Sanderson; Hua Mao 001; Mohammed A. M. Abdullah; Raid Rafi Omar Al-Nima; Wai Lok Woo (Inf., 2023)

Multiview Clustering by Consensus Spectral Rotation Fusion. - Jie Chen 065; Hua Mao 001; Dezhong Peng; Changqing Zhang 002; Xi Peng 001 (IEEE Trans. Image Process., 2023)

Efficient Sparse Representation for Learning With High-Dimensional Data. - Jie Chen 065; Shengxiang Yang; Zhu Wang 007; Hua Mao 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Two-Stage Sparse Representation Clustering for Dynamic Data Streams. - Jie Chen 065; Zhu Wang 007; Shengxiang Yang; Hua Mao 001 (IEEE Trans. Cybern., 2023)

Low-Rank Tensor Learning for Incomplete Multiview Clustering. - Jie Chen 065; Zhu Wang 007; Hua Mao 001; Xi Peng 001 (IEEE Trans. Knowl. Data Eng., 2023)

Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques. - Jacob Sanderson; Hua Mao 001; Naruephorn Tengtrairat; Raid Rafi Omar Al-Nima; Wai Lok Woo (ICAART (3), 2024)

Self-Supervised Conditional Distribution Learning on Graphs. - Jie Chen 065; Hua Mao 001; Yuanbiao Gou; Zhu Wang 007; Xi Peng 001 (CoRR, 2024)

Explainable AI Evaluation: A Top-Down Approach for Selecting Optimal Explanations for Black Box Models. - Seyedeh Roksana Mirzaei; Hua Mao 001; Raid Rafi Omar Al-Nima; Wai Lok Woo (Inf., 2024)

Dynamic Graph Embedding via Meta-Learning. - Yuren Mao; Yu Hao 003; Xin Cao 001; Yixiang Fang; Xuemin Lin 001; Hua Mao 001; Zhiqiang Xu 003 (IEEE Trans. Knowl. Data Eng., 2024)

Cross-View Graph Consistency Learning for Invariant Graph Representations. - Jie Chen 065; Hua Mao 001; Wai Lok Woo; Chuanbin Liu 003; Xi Peng 001 (AAAI, 2025)

DiPACE: Diverse, Plausible and Actionable Counterfactual Explanations. - Jacob Sanderson; Hua Mao 001; Wai Lok Woo (ICAART (2), 2025)

Efficient algorithms for collecting the statistics of large-scale IP address data. - Hui Liu; Yi Cao; Zehan Cai; Hua Mao 001; Jie Chen 065 (Comput. Sci. Inf. Syst., 2025)

Progressive low-confidence pseudolabeling for semisupervised node classification. - Tao Zhu; Hua Mao 001; Hui Liu; Jie Chen 065 (Neurocomputing, 2025)

One-Step Adaptive Graph Learning for Incomplete Multiview Subspace Clustering. - Jie Chen 065; Hua Mao 001; Wai Lok Woo; Chuanbin Liu 003; Zhu Wang 007; Xi Peng 001 (IEEE Trans. Knowl. Data Eng., 2025)

GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks. - Jacob Sanderson; Hua Mao 001; Wai Lok Woo (IEEE Trans. Artif. Intell., 2025)

Hierarchical Sparse Representation Clustering for High-Dimensional Data Streams. - Jie Chen 065; Hua Mao 001; Yuanbiao Gou; Xi Peng 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)

Conditional Distribution Learning for Graph Classification. - Jie Chen 065; Hua Mao 001; Chuanbin Liu 003; Zhu Wang 007; Xi Peng 001 (AAAI, 2026)

Homophilic-aware graph contrastive learning. - Li Zhang; Hua Mao 001; Wai Lok Woo; Jie Chen 065 (Pattern Recognit., 2026)

CACE: A Framework for Generating Counterfactual Explanations Aligned With Causal Structure via Jointly Learned Conditional Distributions. - Jacob Sanderson; Hua Mao 001; Qiuji Yi; Wai Lok Woo (IEEE Trans. Knowl. Data Eng., 2026)