mlsanityData to 14 Oct 2026

Mixture Proportion Estimation Beyond Irreducibility

arXiv 2306.01253 ↗First posted 2 Jun 2023stat.ML cs.LGcited by 9

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Abstract (arXiv)

The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture. Previous work on MPE adopts the irreducibility assumption, which ensures identifiablity of the mixture proportion. In this paper, we propose a more general sufficient condition that accommodates several settings of interest where irreducibility does not hold. We further present a resampling-based meta-algorithm that takes any existing MPE algorithm designed to work under irreducibility and adapts it to work under our more general condition. Our approach empirically exhibits improved estimation performance relative to baseline methods and to a recently proposed regrouping-based algorithm.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

12 of 42 references.

References · 12

  1. 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 219
  2. 2018Learning from positive and unlabeled data: a surveycited by 219
  3. 2016Mixture Proportion Estimation via Kernel Embedding of Distributionscited by 67
  4. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 65
  5. 2021Mixture Proportion Estimation and PU Learning: A Modern Approachcited by 52
  6. 2018Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Datacited by 36
  7. 2018A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptationcited by 23
  8. 2021A linear adjustment based approach to posterior drift in transfer learningcited by 17
  9. 2020Rethinking Class-Prior Estimation for Positive-Unlabeled Learningcited by 15
  10. 2016Nonparametric semi-supervised learning of class proportionscited by 13
  11. 2021Quantifying disparities in intimate partner violence: a machine learning method to correct for underreportingcited by 8
  12. 2013Class Proportion Estimation with Application to Multiclass Anomaly Rejectioncited by 6

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Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2024Label-Noise Robust Diffusion Modelscited by 16
  2. 2024Robust Offline Imitation Learning from Diverse Auxiliary Datacited by 5
  3. 2025Rethinking Consistent Multi-Label Classification Under Inexact Supervisioncited by 3
  4. 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learningcited by 2
  5. 2025Conformal Data Contamination Tests for In-distribution Data Acquisitioncited by 1
  6. 2025Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithmscited by 1
  7. 2025Semiparametric Learning from Open-Set Label Shift Datacited by 0
  8. 2026Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independencecited by 0
  9. 2026Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classificationcited by 0