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arXiv Machine Learning By Jing Ma, Yujia Wu, Zhaoqiang Liu

Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing

Read the original on arXiv Machine Learning →

The paper investigates the sample complexity of noisy one‑bit compressed sensing for signals drawn from a prior distribution. By linking the prior’s effective distributional complexity to its approximate covering number, the authors prove that posterior sampling can recover signals accurately when the number of measurements scales with the logarithm of this covering number, modulo a one‑bit separation gap. They also show that the method remains reliable under learned prior mismatch measured in Wasserstein distance, provide a matching lower bound, and demonstrate practical performance using a diffusion‑based plug‑and‑play algorithm on FFHQ and ImageNet.

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 Machine Learning.

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