Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces
A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, auto-segmenting video and audio recordings, detecting changes in movement direction from motion sensors cannot be fully addressed without detection of distributional shifts in time-ordered data. We consider a sequential change-point detection problem where the conditional density switches at an unknown time, yet neither the pre- nor post-change distribution admits a closed-form. Classical likelihood-ratio statistics are inapplicable in this settings. A conditional diffusion model, trained on pre-change-point data with a frozen context encoder, defines a deterministic bijection via t...
| Authors | Artem Kraevskiy, Artem Prokhorov |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 25 August 2026 |
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