Ziming Zhang

Zhang, X. ., Li, Y. ., Zhang, Z. ., & Zhang, Z.-L. . (2020). f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning. In Advances in neural information processing systems. Retrieved from https://par.nsf.gov/biblio/10290596

Zhang, X. ., Li, Y. ., Zhou, X. ., Zhang, Z. ., & Luo, J. . (2020). TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-Term Decision Analysis. In 2020 IEEE International Conference on Data Mining (ICDM) (p. 10). http://doi.org/10.1109/ICDM50108.2020.00089

Zhang, X. ., Li, Y. ., Zhang, Z. ., Brinton, C. G., Liu, Z. ., & Zhang, Z.-L. . (2023). Distributional Cloning for Stabilized Imitation Learning via ADMM. In (p. 10). IEEE. http://doi.org/10.1109/ICDM58522.2023.00091

Zhang, Y. ., Li, Y. ., Zhou, X. ., Zhang, Z. ., & Luo, J. . (2023). STM-GAIL: Spatial-Temporal Meta-GAIL for Learning Diverse Human Driving Strategies. In 2023 SIAM International Conference on Data Mining (SDM). http://doi.org/10.1137/1.9781611977653.ch24

Zhang, X. ., Li, Y. ., Zhang, Z. ., & Zhang, Z.-L. . (2023). Domain Disentangled Meta-Learning. In 2023 SIAM International Conference on Data Mining (SDM). http://doi.org/10.1137/1.9781611977653.ch61

Ding, Y. ., Zhang, Z. ., Li, Y. ., & Zhou, X. . (2022). EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data. In SIGSPATIAL ’22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems (p. 10). http://doi.org/10.1145/3557915.3560946

Zhang, Z. ., Yue, Y. ., Wu, G. ., Li, Y. ., & Zhang, H. . (2021). SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization. In Advances in Neural Information Processing (NeurIPS 2021) (Vol. 34). Retrieved from https://par.nsf.gov/biblio/10326796

Zhang, X. ., Huang, W. ., Li, Y. ., Liao, R. ., & Zhang, Z. . (2021). Imitation Learning From Inconcurrent Multi-Agent Interactions. In The 60th IEEE Conference on Decision and Control (CDC 2021) (p. 6). http://doi.org/10.1109/CDC45484.2021.9683740

Zhang, Z. ., Wu, G. ., Li, Y. ., Yue, Y. ., & Zhou, X. . (2021). Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System. In 2020 IEEE International Conference on Data Mining (ICDM) (p. 10). http://doi.org/10.1109/ICDM51629.2021.00108

Zhang, X. ., Li, Y. ., Zhou, X. ., Mangoubi, O. ., Zhang, Z. ., Filardi, V. ., & Luo, J. . (2021). DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction. In IEEE International Conference on Data Mining (ICDM). http://doi.org/10.1109/ICDM51629.2021.00102