# Learning Hyper Label Model for Programmatic Weak Supervision

arXiv: https://arxiv.org/abs/2207.13545 · HTML: https://mlsanity.com/paper/2207.13545/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 61 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Cross-Modal Data Programming Enables Rapid Medical Machine Learning](https://mlsanity.com/paper/1903.11101/) (2019)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [GOGGLES: Automatic Image Labeling with Affinity Coding](https://mlsanity.com/paper/1903.04552/) (2019)
- [Incremental Knowledge Base Construction Using DeepDive](https://mlsanity.com/paper/1502.00731/) (2015)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [Learning from Multiple Noisy Partial Labelers](https://mlsanity.com/paper/2106.04530/) (2021)
- [Fonduer: Knowledge Base Construction from Richly Formatted Data](https://mlsanity.com/paper/1703.05028/) (2017)
- [Constrained Labeling for Weakly Supervised Learning](https://mlsanity.com/paper/2009.07360/) (2020)

## Built on by (most cited first, 16 of 16)

- [Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds](https://mlsanity.com/paper/2306.03116/) (2023)
- [Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations](https://mlsanity.com/paper/2305.12715/) (2023)
- [How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking](https://mlsanity.com/paper/2312.01619/) (2023)
- [Ground Truth Inference for Weakly Supervised Entity Matching](https://mlsanity.com/paper/2211.06975/) (2022)
- [Mitigating Source Bias for Fairer Weak Supervision](https://mlsanity.com/paper/2303.17713/) (2023)
- [Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification](https://mlsanity.com/paper/2307.11031/) (2023)
- [Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance](https://mlsanity.com/paper/2401.17897/) (2024)
- [It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation](https://mlsanity.com/paper/2310.00486/) (2023)
- [Fusing Conditional Submodular GAN and Programmatic Weak Supervision](https://mlsanity.com/paper/2312.10366/) (2023)
- [Convergence Behavior of an Adversarial Weak Supervision Method](https://mlsanity.com/paper/2405.16013/) (2024)
- [WeShap: Weak Supervision Source Evaluation with Shapley Values](https://mlsanity.com/paper/2406.11010/) (2024)
- [Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities](https://mlsanity.com/paper/2508.03896/) (2025)
- [RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision](https://mlsanity.com/paper/2307.04870/) (2023)
- [Weak Supervision for Real World Graphs](https://mlsanity.com/paper/2506.02451/) (2025)
- [Unsupervised Ensemble Learning Through Deep Energy-based Models](https://mlsanity.com/paper/2601.20556/) (2026)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)

## Abstract (arXiv)

To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model to aggregate the output of multiple LFs to produce training labels. Most existing label models require a parameter learning step for each dataset. In this work, we present a hyper label model that (once learned) infers the ground-truth labels for each dataset in a single forward pass without dataset-specific parameter learning. The hyper label model approximates an optimal analytical (yet computationally intractable) solution of the ground-truth labels. We train the model on synthetic data generated in the way that ensures the model approximates the analytical optimal solution, and build the model upon Graph Neural Network (GNN) to ensure the model prediction being invariant (or equivariant) to the permutation of LFs (or data points). On 14 real-world datasets, our hyper label model outperforms the best existing methods in both accuracy (by 1.4 points on average) and efficiency (by six times on average). Our code is available at https://github.com/wurenzhi/hyper_label_model