# End-to-End Weak Supervision

arXiv: https://arxiv.org/abs/2107.02233 · HTML: https://mlsanity.com/paper/2107.02233/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 27 of 27)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Understanding self-supervised Learning Dynamics without Contrastive Pairs](https://mlsanity.com/paper/2102.06810/) (2021)
- [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)
- [Deep learning from crowds](https://mlsanity.com/paper/1709.01779/) (2017)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [Who Said What: Modeling Individual Labelers Improves Classification](https://mlsanity.com/paper/1703.08774/) (2017)
- [Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing](https://mlsanity.com/paper/1406.3824/) (2014)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (2020)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [Learning From Noisy Singly-labeled Data](https://mlsanity.com/paper/1712.04577/) (2017)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Learning Dependency Structures for Weak Supervision Models](https://mlsanity.com/paper/1903.05844/) (2019)
- [Cross-Modal Data Programming Enables Rapid Medical Machine Learning](https://mlsanity.com/paper/1903.11101/) (2019)
- [Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds](https://mlsanity.com/paper/1905.13436/) (2019)
- [Inferring Generative Model Structure with Static Analysis](https://mlsanity.com/paper/1709.02477/) (2017)
- [Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction](https://mlsanity.com/paper/1712.02734/) (2017)
- [Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision](https://mlsanity.com/paper/1808.08485/) (2018)
- [Data Programming using Continuous and Quality-Guided Labeling Functions](https://mlsanity.com/paper/1911.09860/) (2019)
- [Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation](https://mlsanity.com/paper/2103.02761/) (2021)
- [Dependency Structure Misspecification in Multi-Source Weak Supervision Models](https://mlsanity.com/paper/2106.10302/) (2021)

## Built on by (most cited first, 30 of 33)

- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Theoretical Analysis of Weak-to-Strong Generalization](https://mlsanity.com/paper/2405.16043/) (2024)
- [Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision](https://mlsanity.com/paper/2203.13270/) (2022)
- [CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation](https://mlsanity.com/paper/2603.00039/) (2026)
- [Losses over Labels: Weakly Supervised Learning via Direct Loss Construction](https://mlsanity.com/paper/2212.06921/) (2022)
- [Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds](https://mlsanity.com/paper/2306.03116/) (2023)
- [Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming](https://mlsanity.com/paper/2203.01382/) (2022)
- [Multi-annotator Deep Learning: A Probabilistic Framework for Classification](https://mlsanity.com/paper/2304.02539/) (2023)
- [MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection](https://mlsanity.com/paper/2205.09817/) (2022)
- [Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification](https://mlsanity.com/paper/2210.03092/) (2022)
- [Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision](https://mlsanity.com/paper/2210.02724/) (2022)
- [WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning](https://mlsanity.com/paper/2212.10057/) (2022)
- [Ground Truth Inference for Weakly Supervised Entity Matching](https://mlsanity.com/paper/2211.06975/) (2022)
- [Pareto Optimal Learning for Estimating Large Language Model Errors](https://mlsanity.com/paper/2306.16564/) (2023)
- [SepLL: Separating Latent Class Labels from Weak Supervision Noise](https://mlsanity.com/paper/2210.13898/) (2022)
- [Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach](https://mlsanity.com/paper/2302.01887/) (2023)
- [Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision](https://mlsanity.com/paper/2306.07036/) (2023)
- [Weak Supervision Performance Evaluation via Partial Identification](https://mlsanity.com/paper/2312.04601/) (2023)
- [Evaluating Generalizability of Fine-Tuned Models for Fake News Detection](https://mlsanity.com/paper/2205.07154/) (2022)
- [Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension](https://mlsanity.com/paper/2405.03386/) (2024)
- [dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans](https://mlsanity.com/paper/2407.20950/) (2024)
- [ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision](https://mlsanity.com/paper/2204.06863/) (2022)
- [XPASC: Measuring Generalization in Weak Supervision by Explainability and Association](https://mlsanity.com/paper/2206.01444/) (2022)
- [Time-Aware Datasets are Adaptive Knowledgebases for the New Normal](https://mlsanity.com/paper/2211.12508/) (2022)
- [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)
- [Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark](https://mlsanity.com/paper/2410.15772/) (2024)
- [Medifact at PerAnsSumm 2025: Leveraging Lightweight Models for Perspective-Specific Summarization of Clinical Q&A Forums](https://mlsanity.com/paper/2503.16513/) (2025)
- [Learning from Interval Targets](https://mlsanity.com/paper/2510.20925/) (2025)
- [Generative Modeling Helps Weak Supervision (and Vice Versa)](https://mlsanity.com/paper/2203.12023/) (2022)

## Abstract (arXiv)

Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels. Current state of the art approaches that do not use any labeled training data, however, require two separate modeling steps: Learning a probabilistic latent variable model based on the WS sources -- making assumptions that rarely hold in practice -- followed by downstream model training. Importantly, the first step of modeling does not consider the performance of the downstream model. To address these caveats we propose an end-to-end approach for directly learning the downstream model by maximizing its agreement with probabilistic labels generated by reparameterizing previous probabilistic posteriors with a neural network. Our results show improved performance over prior work in terms of end model performance on downstream test sets, as well as in terms of improved robustness to dependencies among weak supervision sources.