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arXiv AI By Thomas Flynn, Sanket Jantre, Byung-Jun Yoon, Kibaek Kim

Compressed Active Subspaces for Scalable Bayesian Inference

Read the original on arXiv AI →

The paper introduces Compressed Active Subspaces (CAS), a scalable method for Bayesian inference in high‑dimensional models. CAS first compresses model parameters via a structured isometric embedding, then constructs the active subspace in this reduced space, dramatically lowering memory requirements. Experiments on neural networks of growing size show that CAS preserves predictive performance and provides robust uncertainty estimates while enabling inference where traditional active subspace methods fail.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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