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Conformalized Rate-Adaptive Sensing

arXiv 2607.26887 ↗First posted 29 Jul 2026stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

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.

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References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2022Conformal Risk Controlcited by 290
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  5. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  6. 2021Localized Conformal Prediction: A Generalized Inference Framework for Conformal Predictioncited by 112
  7. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  8. 2024Theoretical Foundations of Conformal Predictioncited by 96
  9. 2023Conformal prediction with local weights: randomization enables local guaranteescited by 47
  10. 2020Active MR k-space Sampling with Reinforcement Learningcited by 45
  11. 2018Synthetic Difference in Differencescited by 41
  12. 2020Experimental design for MRI by greedy policy searchcited by 40
4 more
  1. 2018The Augmented Synthetic Control Methodcited by 25
  2. 2024Conformal Prediction with Learned Featurescited by 23
  3. 2024Posterior Conformal Predictioncited by 6
  4. 2026Statistical Early Stopping for Reasoning Modelscited by 4

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