mlsanityData to 14 Oct 2026

Learning from M-Tuple Dominant Positive and Unlabeled Data

arXiv 2506.15686 ↗First posted 25 May 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Label Proportion Learning (LLP) addresses the classification problem where multiple instances are grouped into bags and each bag contains information about the proportion of each class. However, in practical applications, obtaining precise supervisory information regarding the proportion of instances in a specific class is challenging. To better align with real-world application scenarios and effectively leverage the proportional constraints of instances within tuples, this paper proposes a generalized learning framework MDPU. Specifically, we first mathematically model the distribution of instances within tuples of arbitrary size, under the constraint that the number of positive instances is no less than that of negative instances. Then we derive an unbiased risk estimator that satisfies risk consistency based on the empirical risk minimization (ERM) method. To mitigate the inevitable overfitting issue during training, a risk correction method is introduced, leading to the development of a corrected risk estimator. The generalization error bounds of the unbiased risk estimator theoretically demonstrate the consistency of the proposed method. Extensive experiments on multiple datasets and comparisons with other relevant baseline methods comprehensively validate the effectiveness of the proposed learning framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 60 references that are in the map. First posted on arXiv 25 May 2025.

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23 of 60 references.

References · 23

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
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  10. 2020Weakly Supervised Learning with Side Information for Noisy Labeled Imagescited by 30
  11. 2019Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approachcited by 29
  12. 2017Binary Classification from Positive-Confidence Datacited by 23
11 more
  1. 2019Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimizationcited by 21
  2. 2020Pointwise Binary Classification with Pairwise Confidence Comparisonscited by 20
  3. 2021Learning from Similarity-Confidence Datacited by 19
  4. 2023Binary Classification with Confidence Differencecited by 13
  5. 2021Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classificationcited by 12
  6. 2016Efficient Training for Positive Unlabeled Learningcited by 10
  7. 2024Impact of Noisy Supervision in Foundation Model Learningcited by 8
  8. 2013proptoSVM for learning with label proportionscited by 7
  9. 2022Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularizationcited by 6
  10. 2023Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimationcited by 4
  11. 2022Learning from Positive and Unlabeled Data with Augmented Classescited by 1

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