Representation Learning for Spatial Graphs. - Zheng Wang 046; Ce Ju; Gao Cong; Cheng Long 001 (CoRR, 2018)

Privacy-preserving Heterogeneous Federated Transfer Learning. - Dashan Gao 002; Yang Liu 165; Anbu Huang; Ce Ju; Han Yu 001; Qiang Yang 001 (IEEE BigData, 2019)

Effective and Efficient Sports Play Retrieval with Deep Representation Learning. - Zheng Wang 046; Cheng Long 001; Gao Cong; Ce Ju (KDD, 2019)

Stochastic Inverse Reinforcement Learning. - Ce Ju; Dong Eui Chang (CoRR, 2019)

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography. - Dashan Gao 002; Ce Ju; Xiguang Wei; Yang Liu 165; Tianjian Chen; Qiang Yang 001 (CoRR, 2019)

Federated Transfer Learning for EEG Signal Classification. - Ce Ju; Dashan Gao 002; Ravikiran Mane; Ben Tan; Yang Liu 165; Cuntai Guan (EMBC, 2020)

Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes. - Lixin Fan; Kam Woh Ng; Ce Ju; Tianyu Zhang; Chee Seng Chan (IJCAI, 2020)

Interaction-aware Kalman Neural Networks for Trajectory Prediction. - Ce Ju; Zheng Wang 046; Cheng Long 001; Xiaoyu Zhang; Dong Eui Chang (IV, 2020)

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention. - Ce Ju; Ruihui Zhao; Jichao Sun; Xiguang Wei; Bo Zhao; Yang Liu 165; Hongshan Li; Tianjian Chen; Xinwei Zhang 001; Dashan Gao 002; Ben Tan; Han Yu 001; et al. (CoRR, 2020)

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks. - Lixin Fan; Kam Woh Ng; Ce Ju; Tianyu Zhang; Chang Liu 069; Chee Seng Chan; Qiang Yang 001 (CoRR, 2020)

Privacy Threats Against Federated Matrix Factorization. - Dashan Gao 002; Ben Tan; Ce Ju; Vincent W. Zheng; Qiang Yang 001 (CoRR, 2020)

Geometric Foundations of Data Reduction. - Ce Ju (CoRR, 2020)

Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness. - Yilun Jin; Lixin Fan; Kam Woh Ng; Ce Ju; Qiang Yang 001 (CoRR, 2020)

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks. - Lixin Fan; Kam Woh Ng; Ce Ju; Tianyu Zhang; Chang Liu 069; Chee Seng Chan; Qiang Yang 001 (Federated Learning, 2020)

Ternary Hashing. - Chang Liu 069; Lixin Fan; Kam Woh Ng; Yilun Jin; Ce Ju; Tianyu Zhang; Chee Seng Chan; Qiang Yang 001 (CoRR, 2021)

Deep Optimal Transport on SPD Manifolds for Domain Adaptation. - Ce Ju; Cuntai Guan (CoRR, 2022)

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance. - Ce Ju; Reinmar Josef Kobler; Cuntai Guan (EMBC, 2023)

Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification. - Ce Ju; Cuntai Guan (IEEE Trans. Neural Networks Learn. Syst., 2023)

Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data. - Ce Ju; Reinmar J. Kobler; Liyao Tang; Cuntai Guan; Motoaki Kawanabe (ICLR, 2024)

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis. - Ce Ju; Cuntai Guan (IEEE Trans. Neural Networks Learn. Syst., 2024)

Deep optimal transport for domain adaptation on SPD manifolds. - Ce Ju; Cuntai Guan (Artif. Intell., 2025)

SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges. - Ce Ju; Reinmar J. Kobler; Antoine Collas; Motoaki Kawanabe; Cuntai Guan; Bertrand Thirion (CoRR, 2025)

Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry. - Antoine Collas; Ce Ju; Nicolas Salvy; Bertrand Thirion (CoRR, 2025)