mlsanityData to 7 Oct 2026

Active Measurement: Efficient Estimation at Scale

arXiv 2507.01372 ↗First posted 2 Jul 2025cs.CV cs.LGcited by 4

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Abstract (arXiv)

AI has the potential to transform scientific discovery by analyzing vast datasets with little human effort. However, current workflows often do not provide the accuracy or statistical guarantees that are needed. We introduce active measurement, a human-in-the-loop AI framework for scientific measurement. An AI model is used to predict measurements for individual units, which are then sampled for human labeling using importance sampling. With each new set of human labels, the AI model is improved and an unbiased Monte Carlo estimate of the total measurement is refined. Active measurement can provide precise estimates even with an imperfect AI model, and requires little human effort when the AI model is very accurate. We derive novel estimators, weighting schemes, and confidence intervals, and show that active measurement reduces estimation error compared to alternatives in several measurement tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 2 Jul 2025.

Builds on

16 of 43 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2023Prediction-Powered Inferencecited by 214
  4. 2021Learning To Count Everythingcited by 149
  5. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
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  8. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
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  10. 2021Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxiescited by 40
  11. 2016Crowdsourcing in Computer Visioncited by 31
  12. 2022The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Countingcited by 24
4 more
  1. 2018Active Testing: An Efficient and Robust Framework for Estimating Accuracycited by 15
  2. 2021IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Samplingcited by 14
  3. 2018Asymptotic optimality of adaptive importance samplingcited by 9
  4. 2023DISCount: Counting in Large Image Collections with Detector-Based Importance Samplingcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Active Measurement of Two-Point Correlationscited by 0
  2. 2026BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judgescited by 0
  3. 2026Scalable Model-Assisted Multi-Target Estimation in Large Image Collectionscited by 0
  4. 2026Speculative Evaluation of Stochastic LLMscited by 0