# Pairwise Feedback for Data Programming

@article{Boecking2019PairwiseFF, title={Pairwise Feedback for Data Programming}, author={Benedikt Boecking and Artur W. Dubrawski}, journal={ArXiv}, year={2019}, volume={abs/1912.07685} }

The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of labeled datasets via the synthesis of noisy heuristics provides a promising avenue to address this problem. We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process. We discuss the ease with which such pairwise feedback…

## 5 Citations

### Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

- Computer ScienceICLR
- 2021

This work develops the first framework for interactive weak supervision in which a method proposes heuristics and learns from user feedback given on each proposed heuristic, demonstrating that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

### Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings

- Computer ScienceArXiv
- 2020

This work borrowing from weak supervision, wherein models can be trained with noisy sources of signal instead of hand-labeled data, outperforms WS without extension, TL without fine-tuning, and state-of-the-art weakly-supervised deep networks-all while training in less than half a second.

### Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision

- Computer ScienceUAI
- 2022

This work proposes L IGER, a combination that uses foundation model embeddings to improve two crucial elements of existing weak supervision techniques, and produces finer estimates of weak source quality by partitioning the embedding space and learning per-part source accuracies.

### The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming

- Computer ScienceArXiv
- 2021

This paper surveys recent work on weak supervision, and in particular, investigates the Data Programming (DP) framework, taking a set of potentially noisy heuristics as input and analyzes the math fundamentals behind DP and demonstrates the power of it by application on two real-world text classification tasks.

### Classifying Unstructured Clinical Notes via Automatic Weak Supervision

- Computer ScienceMLHC
- 2022

This work introduces a general weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents, and leverages the linguistic domain knowledge stored within pre-trained language models and the data programming framework to assign code labels to individual texts.

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