ArXiv

Conformalized Rate-Adaptive Sensing

2026-07-29 · 13:16 UTC ·Jiawei Yang, Yao Zhang ·1 min read
Authors
Jiawei Yang, Yao Zhang
Categories
stat.ML, cs.LG, stat.AP, stat.ME
arXiv
https://arxiv.org/abs/2607.26887v1
PDF
https://arxiv.org/pdf/2607.26887v1

Brief

Conformalized Rate-Adaptive Sensing (CoRAS) tackles the problem of deciding, per image, when enough measurements have been collected so that reconstruction error is below a target with high probability. It tracks a reconstruction path as measurements accrue, estimates the first crossing time (stopping time) from early-path behavior, and calibrates that estimate using similar images to produce an upper bound with marginal and approximate conditional coverage. Empirically, CoRAS achieves the target coverage, reduces average measurement use relative to fixed-rate stopping, and assigns more measurements to harder images. Summary based on the abstract only; full paper (47 pages) is available at arXiv:2607.26887v1.

Why it matters

Conformalized Rate-Adaptive Sensing (CoRAS) adaptively selects an acquisition/compression rate per image and provides an upper bound on the image-specific stopping time (first rate at which reconstruction error falls below a target) with marginal and approximate conditional coverage guarantees.

Key details

  • CoRAS estimates the stopping time from an early reconstruction path and then calibrates that estimate using images with similar early behavior; experiments report that CoRAS attains the target stopping-time coverage, uses fewer measurements on average than fixed-rate stopping rules, and allocates more measurements to harder-to-reconstruct images.
  • Paper metadata: Jiawei Yang and Yao Zhang, arXiv:2607.26887v1 (published 2026-07-29), 47 pages and 8 figures; categories stat.ML, cs.LG, stat.AP, stat.ME. Full text was not available to this summary (abstract-only used).
Source evidence

Abstract

Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method that adaptively chooses an acquisition or compression rate for each image while keeping the reconstruction error below a target level with high probability. As measurements are collected, an image reconstruction model gradually recovers the true image, producing a reconstruction path over acquisition rates. CoRAS uses this path up to an early decision time to estimate the target stopping time, defined as the first time at which the reconstruction error falls below the target level. It then calibrates this estimate using images with similar early reconstruction behavior, producing an upper bound on the stopping time with marginal and approximate conditional coverage guarantees. Experiments on image datasets show that CoRAS attains the target stopping-time coverage, uses fewer measurements on average than fixed-rate stopping rules, and assigns more measurements to images that are harder to reconstruct.

Comment: 47 pages, 8 figures