Compressed Active Subspaces for Scalable Bayesian Inference
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions that have the greatest influence on the model output. However, the construction of active subspaces requires storing many full-dimensional model gradients, which becomes prohibitive as model size increases. We address this limitation by proposing Compressed Active Subspaces (CAS), a scalable approach that first maps the model parameters to a compressed space using a structured isometric embedding and then constructs the active subspace within this reduced parameterization. Our approach substantially reduces the mem...
| Authors | Thomas Flynn, Sanket Jantre, Byung-Jun Yoon et al. |
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| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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