mlsanityData to 7 Oct 2026

Predictive Inference with Weak Supervision

arXiv 2201.08315 ↗First posted 20 Jan 2022stat.ML cs.LGcited by 13

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Abstract (arXiv)

The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage such data for model fitting or validation. We present a methodology to bridge the gap between partial supervision and validation, developing a conformal prediction framework to provide valid predictive confidence sets -- sets that cover a true label with a prescribed probability, independent of the underlying distribution -- using weakly labeled data. To do so, we introduce a (necessary) new notion of coverage and predictive validity, then develop several application scenarios, providing efficient algorithms for classification and several large-scale structured prediction problems. We corroborate the hypothesis that the new coverage definition allows for tighter and more informative (but valid) confidence sets through several experiments.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 23 of its 63 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

23 of 63 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  4. 2019Conformalized Quantile Regressioncited by 620
  5. 2019Conformal Prediction Under Covariate Shiftcited by 511
  6. 2020Classification with Valid and Adaptive Coveragecited by 390
  7. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 370
  8. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
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  10. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  11. 2015Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentationcited by 189
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11 more
  1. 2013Introducing LETOR 4.0 Datasetscited by 120
  2. 2019Nested conformal prediction and quantile out-of-bag ensemble methodscited by 88
  3. 2020Robust Validation: Confident Predictions Even When Distributions Shiftcited by 82
  4. 2013Large-scale Multi-label Learning with Missing Labelscited by 58
  5. 2019With Malice Towards None: Assessing Uncertainty via Equalized Coveragecited by 43
  6. 2018Cautious Deep Learningcited by 35
  7. 2020Structured Prediction with Partial Labelling through the Infimum Losscited by 34
  8. 2019Named Entity Recognition with Partially Annotated Training Datacited by 27
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 1; 2026: 4.

13 papers.

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  1. 2023Conformal Language Modelingcited by 163
  2. 2023Adaptive conformal classification with noisy labelscited by 24
  3. 2023Conformal Prediction for Network-Assisted Regressioncited by 14
  4. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  5. 2024Robust Conformal Prediction Using Privileged Informationcited by 7
  6. 2022The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refittingcited by 4
  7. 2024Predictive Inference in Multi-environment Scenarioscited by 4
  8. 2022Query-Adaptive Predictive Inference with Partial Labelscited by 0
  9. 2025Split Conformal Classification with Unsupervised Calibrationcited by 0
  10. 2026Mean Estimation from Coarse Data: Characterizations and Efficient Algorithmscited by 0
  11. 2026Elements of Conformal Predictioncited by 0
  12. 2026Set-Valued Policy Learningcited by 0
1 more
  1. 2026CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety stratacited by 0