ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first and last frames) is a fundamental challenge. Existing approaches, (e.g., diffusion models), suffer from key limitations: (1) noise-to-data evolution fails to capture structural similarity between states close in physical time and has unstable integration in low-step regimes; (2) random noise injected is insensitive to the physical process's time elapsed, resulting in incorrect dynamics; (3) they overlook conditioning on arbitrary subsets of states (e.g., irregularly sampled timesteps, future observations). We propose ABC: Any-Subset Autoregre...
| Authors | Gabe Guo, Thanawat Sornwanee, Lutong Hao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 28 August 2026 |
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