GAN Ensemble for Anomaly Detection. - Xu Han 012; Xiaohui Chen; Li-Ping Liu 001 (AAAI, 2021)

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. - Xiaohui Chen; Xu Han 012; Jiajing Hu; Francisco J. R. Ruiz; Li-Ping Liu 001 (ICML, 2021)

Predicting Physics in Mesh-reduced Space with Temporal Attention. - Xu Han 012; Han Gao 005; Tobias Pfaff; Jian-Xun Wang 001; Liping Liu 001 (ICLR, 2022)

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. - Han Gao 005; Xu Han 012; Jiaoyang Huang; Jian-Xun Wang 001; Liping Liu 001 (LoG, 2022)

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. - Linfeng Liu 004; Xu Han 012; Dawei Zhou; Li-Ping Liu 001 (Trans. Mach. Learn. Res., 2022)

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. - Xiaohui Chen; Jiaxing He; Xu Han 012; Liping Liu 001 (ICML, 2023)

Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. - Luning Sun; Xu Han 012; Han Gao 005; Jian-Xun Wang 001; Liping Liu 001 (NeurIPS, 2023)

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. - Han Gao 005; Xu Han 012; Xiantao Fan; Luning Sun; Li-Ping Liu 001; Lian Duan; Jian-Xun Wang 001 (CoRR, 2023)

Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. - Xu Han 012; Xiaohui Chen; Francisco J. R. Ruiz; Li-Ping Liu 001 (J. Mach. Learn. Res., 2023)