Tian Han

• PhD (2019) University of California, Los Angeles (Statistics)
• Other (2013) The Hong Kong University of Science and Technology (Computer Science)

• Transfer Credit Coordinator Member

Conference Proceeding

1. Li, H.; Han, T. (2024). Enforcing Sparsity on Latent Space for Robust and Explainable Representations (pp. 5282-5291). Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024).
   https://openaccess.thecvf.com/content/WACV2024/papers/Li_Enforcing_Sparsity_on_Latent_Space_for_Robust_and_Explainable_Representations_WACV_2024_paper.pdf.
2. Cui, J.; Wu, Y.; Han, T. (2023). Learning Hierarchical Features with Joint Latent Space Energy-Based Prior (pp. 2218--2227). Proceedings of IEEE International Conference on Computer Vision (ICCV 2023).
   https://openaccess.thecvf.com/content/ICCV2023/papers/Cui_Learning_Hierarchical_Features_with_Joint_Latent_Space_Energy-Based_Prior_ICCV_2023_paper.pdf.
3. Cui, J.; Han, T. (2023). Learning Energy-based Model via Dual-MCMC Teaching (vol. 36, pp. 28861-28872). Advances in Neural Information Processing Systems (NeurIPS 2023).
   https://proceedings.neurips.cc/paper_files/paper/2023/file/5bed8703db85ab27dc32f6a42f8fbdb6-Paper-Conference.pdf.
4. Kong, D.; Pang, B.; Han, T.; Wu, Y. (2023). Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. Uncertainty in Artificial Intelligence (UAI 2023) (pp. 1109-1120). PMLR.
   https://proceedings.mlr.press/v216/kong23a/kong23a.pdf.
5. Cui, J.; Wu, Y.; Han, T.. Learning Joint Latent Space EBM Prior Model for Multi-layer Generator (pp. 3603-3612). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023).
   https://doi.org/10.1109/CVPR52729.2023.00351.
6. Xiao, Z.; Han, T. (2022). Adaptive multi-stage density ratio estimation for learning latent space energy-based model (vol. 35, pp. 21590-21601). Advances in Neural Information Processing Systems (NeurIPS 2022).
   https://proceedings.neurips.cc/paper_files/paper/2022/file/874a4d89f2d04b4bcf9a2c19545cf040-Paper-Conference.pdf.
7. Lu, C.; Han, T.; Ning, Y. (2022). Context-aware health event prediction via transition functions on dynamic disease graphs. Proceedings of the AAAI Conference on Artificial Intelligence (4 ed., vol. 36, pp. 4567-4574). AAAI.
   https://www.aaai.org/AAAI22Papers/AAAI-6800.LuC.pdf.
8. Zhao, Y.; Qiu, L.; Lu, P.; Shi, F.; Han, T.; Zhu, S. (2022). Learning from the Tangram to Solve Mini Visual Tasks (3 ed., vol. 36, pp. 3490--3498). The Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022.
   https://www.aaai.org/AAAI22Papers/AAAI-10312.ZhaoY.pdf.
9. Pang, B.; Han, T.; Nijkamp, E.; Zhu, S.; Wu, Y. (2020). Learning Latent Space Energy-Based Prior Model. Advances in Neural Information Processing Systems (NeurIPS 2020).
10. Nijkamp, E.; Pang, B.; Han, T.; Zhou, L.; Zhu, S.; Wu, Y. (2020). Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. 16th European Conference on Computer Vision, ECCV 2020.
11. Han, T.; Nijkamp, E.; Zhou, L.; Pang, B.; Zhu, S.; Wu, Y. (2020). Joint Training of Variational Auto-Encoder and Latent Energy-Based Model (pp. 7978--7987). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020.
12. Nijkamp, E.; Hill, M.; Han, T.; Zhu, S.; Wu, Y. (2020). On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models (pp. 5272--5280). The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020.
13. Han, T.; Nijkamp, E.; Fang, X.; Hill, M.; Zhu, S.; Wu, Y. (2019). Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inferential Model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019.
14. Han, T.; Lu, Y.; Zhu, S.; Wu, Y. (2017). Alternating Back-Propagation for Generator Network. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).

Conference Workshop Contribution

1. Han, T.; Zhang, J.; Wu, Y. (2020). From em-projection to Variational Auto-Encoder. NeurIPS 2020 Workshop on Deep Learning through Information Geometry.

Journal Article

1. Xing, X.; Gao, R.; Han, T.; Zhu, S.; Wu, Y. (2020). Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. Transactions on Pattern Analysis and Machine Intelligence (PAMI). IEEE.