# Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

arXiv: https://arxiv.org/abs/2210.02724 · HTML: https://mlsanity.com/paper/2210.02724/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [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)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [Denoising Multi-Source Weak Supervision for Neural Text Classification](https://mlsanity.com/paper/2010.04582/) (2020)
- [End-to-End Weak Supervision](https://mlsanity.com/paper/2107.02233/) (2021)
- [Universalizing Weak Supervision](https://mlsanity.com/paper/2112.03865/) (2021)
- [Inferring Generative Model Structure with Static Analysis](https://mlsanity.com/paper/1709.02477/) (2017)
- [Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation](https://mlsanity.com/paper/1905.09670/) (2019)
- [Learning from Multiple Noisy Partial Labelers](https://mlsanity.com/paper/2106.04530/) (2021)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Dependency Structure Misspecification in Multi-Source Weak Supervision Models](https://mlsanity.com/paper/2106.10302/) (2021)
- [Binary Classification with Positive Labeling Sources](https://mlsanity.com/paper/2208.01704/) (2022)

## Built on by (most cited first, 6 of 6)

- [Towards Acyclic Preference Evaluation of Language Models via Multiple Evaluators](https://mlsanity.com/paper/2410.12869/) (2024)
- [Multimodal Data Curation via Object Detection and Filter Ensembles](https://mlsanity.com/paper/2401.12225/) (2024)
- [Mitigating Source Bias for Fairer Weak Supervision](https://mlsanity.com/paper/2303.17713/) (2023)
- [Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance](https://mlsanity.com/paper/2401.17897/) (2024)
- [Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities](https://mlsanity.com/paper/2508.03896/) (2025)
- [Local Boosting for Weakly-Supervised Learning](https://mlsanity.com/paper/2306.02859/) (2023)

## Abstract (arXiv)

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources abstracted as labeling functions (LFs). Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process. In this paper, we attempt to incorporate the instance features into a statistical label model via the proposed FABLE. In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features. We adopt an auxiliary variable-based variational inference algorithm to tackle the non-conjugate issue between the Gaussian Process and Bayesian label models. Extensive empirical comparison on eleven benchmark datasets sees FABLE achieving the highest averaged performance across nine baselines.