mlsanityData to 14 Oct 2026

Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization

arXiv 2302.08947 ↗First posted 17 Feb 2023cs.CVcited by 9

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Abstract (arXiv)

This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large. We demonstrate the effectiveness of the proposed method using some benchmark datasets.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

12 of 27 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  3. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  4. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  5. 2019Introduction to Online Convex Optimizationcited by 803
  6. 2019Learning from Label Proportions with Consistency Regularizationcited by 34
  7. 2019Deep multi-class learning from label proportionscited by 33
  8. 2017Co-training for Demographic Classification Using Deep Learning from Label Proportionscited by 28
  9. 2019Learning from Label Proportions with Generative Adversarial Networkscited by 25
  10. 2021Two-stage Training for Learning from Label Proportionscited by 19
  11. 2020Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathologycited by 12
  12. 2020No Regret Sample Selection with Noisy Labelscited by 4

Built on by

Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2023MixBag: Bag-Level Data Augmentation for Learning from Label Proportionscited by 8
  2. 2024Learning from Partial Label Proportions for Whole Slide Image Segmentationcited by 6
  3. 2024Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bagscited by 2
  4. 2024Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportionscited by 2
  5. 2025Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labelingcited by 2
  6. 2026Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learningcited by 0
  7. 2026Learning from Label Proportions with Dual-proportion Constraintscited by 0
  8. 2026Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportionscited by 0
  9. 2026Nous: Learning and Certifying Memory Decisions Before Source Calibrationcited by 0