mlsanityData to 7 Oct 2026

Learning from Rules Generalizing Labeled Exemplars

arXiv 2004.06025 ↗First posted 13 Apr 2020cs.LG cs.CL stat.MLcited by 59

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Abstract (arXiv)

In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. The denoised rules and trained model are used jointly for inference. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2020.

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Citing papers per year: 2021: 11; 2022: 19; 2023: 16; 2024: 8; 2025: 3; 2026: 2.

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  1. 2023DataComp: In search of the next generation of multimodal datasetscited by 635
  2. 2021WRENCH: A Comprehensive Benchmark for Weak Supervisioncited by 95
  3. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 93
  4. 2021When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Datacited by 75
  5. 2022A Survey on Programmatic Weak Supervisioncited by 66
  6. 2021Self-Training with Weak Supervisioncited by 49
  7. 2021End-to-End Weak Supervisioncited by 33
  8. 2023Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidancecited by 24
  9. 2021DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samplescited by 18
  10. 2021Learning from Multiple Noisy Partial Labelerscited by 14
  11. 2022PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learningcited by 14
  12. 2021Active WeaSuL: Improving Weak Supervision with Active Learningcited by 12
38 more
  1. 2021Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programmingcited by 11
  2. 2022Losses over Labels: Weakly Supervised Learning via Direct Loss Constructioncited by 11
  3. 2025Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistancecited by 11
  4. 2022Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programmingcited by 10
  5. 2023DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modelingcited by 10
  6. 2024Learning with Logical Constraints but without Shortcut Satisfactioncited by 10
  7. 2022Label Propagation with Weak Supervisioncited by 9
  8. 2023Distilling Rule-based Knowledge into Large Language Modelscited by 9
  9. 2023How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Rankingcited by 8
  10. 2021Self-Training with Differentiable Teachercited by 7
  11. 2022Meta Self-Refinement for Robust Learning with Weak Supervisioncited by 7
  12. 2021SPEAR : Semi-supervised Data Programming in Pythoncited by 6
  13. 2022Automatic Rule Induction for Interpretable Semi-Supervised Learningcited by 6
  14. 2022WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learningcited by 6
  15. 2022Learning to Detect Noisy Labels Using Model-Based Featurescited by 6
  16. 2023PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Trainingcited by 6
  17. 2022Adaptive Mixing of Auxiliary Losses in Supervised Learningcited by 5
  18. 2023Pareto Optimal Learning for Estimating Large Language Model Errorscited by 5
  19. 2022Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Predictioncited by 4
  20. 2022SepLL: Separating Latent Class Labels from Weak Supervision Noisecited by 4
  21. 2023Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removalcited by 4
  22. 2023Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervisioncited by 4
  23. 2022FROTE: Feedback Rule-Driven Oversampling for Editing Modelscited by 3
  24. 2022Data Consistency for Weakly Supervised Learningcited by 2
  25. 2023EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Imagescited by 2
  26. 2024Interactive Machine Teaching by Labeling Rules and Instancescited by 2
  27. 2022Label Augmentation with Reinforced Labeling for Weak Supervisioncited by 1
  28. 2022ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervisioncited by 1
  29. 2022XPASC: Measuring Generalization in Weak Supervision by Explainability and Associationcited by 1
  30. 2023Current and Future Challenges in Knowledge Representation and Reasoningcited by 1
  31. 2023Fusing Conditional Submodular GAN and Programmatic Weak Supervisioncited by 1
  32. 2024Leveraging Large Language Models for Structure Learning in Prompted Weak Supervisioncited by 1
  33. 2024ActiveDP: Bridging Active Learning and Data Programmingcited by 1
  34. 2024FAIR: Filtering of Automatically Induced Rulescited by 1
  35. 2024Convergence Behavior of an Adversarial Weak Supervision Methodcited by 1
  36. 2024A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)cited by 1
  37. 2024A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Mediacited by 1
  38. 2025Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilitiescited by 1

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