Bayesian Tensor Decomposition with Diffusion Model Prior
Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often degrades under severe missingness or noise. Low-rankness is itself a useful but limited structural prior, and additional handcrafted priors (e.g., sparsity or smoothness) still fall short of capturing the rich statistics of real-world data. To compensate for this weak inductive bias under heavy corruption, one would like to inject a learned, data-driven prior; however, the state-of-the-art diffusion models are not readily compatible with current TD and tractable posterior inference. To address these challenges, we introduce DiffBCP, a hybrid-prior Bayesian CP decomposition framework that co...
| Authors | Zerui Tao, Qibin Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 2 June 2026 |
| Last updated at source | 15 June 2026 |
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