3 papers
cs.LG2026
Inference-Time Attribute Distribution Alignment for Unconditional Diffusion
Hao Luan, See-Kiong Ng, Chun Kai Ling
Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques focus on individual samples, st…
cs.LG2026
Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Hao Luan, Yi Xian Goh, See-Kiong Ng +1
Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process to achieve a given objective wit…
cs.LG2025
DDPS: Discrete Diffusion Posterior Sampling for Paths in Layered Graphs
Hao Luan, See-Kiong Ng, Chun Kai Ling
Diffusion models form an important class of generative models today, accounting for much of the state of the art in cutting edge AI research. While numerous extensions beyond image…