2 papers
cs.LG2025
Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou, Shaoan Xie, Guang-Yuan Hao +7
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have prov…
cs.LG2024
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
Huandong Wang, Changzheng Gao, Yuchen Wu +3
Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. Howeve…