# biquality-learn: a Python library for Biquality Learning

arXiv: https://arxiv.org/abs/2308.09643 · HTML: https://mlsanity.com/paper/2308.09643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Learning with Symmetric Label Noise: The Importance of Being Unhinged](https://mlsanity.com/paper/1505.07634/) (2015)
- [Rethinking Importance Weighting for Deep Learning under Distribution Shift](https://mlsanity.com/paper/2006.04662/) (2020)
- [SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python](https://mlsanity.com/paper/1907.10121/) (2019)
- [Part-dependent Label Noise: Towards Instance-dependent Label Noise](https://mlsanity.com/paper/2006.07836/) (2020)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [From Weakly Supervised Learning to Biquality Learning: an Introduction](https://mlsanity.com/paper/2012.09632/) (2020)
- [Importance Reweighting for Biquality Learning](https://mlsanity.com/paper/2010.09621/) (2020)

## Abstract (arXiv)

The democratization of Data Mining has been widely successful thanks in part to powerful and easy-to-use Machine Learning libraries. These libraries have been particularly tailored to tackle Supervised Learning. However, strong supervision signals are scarce in practice, and practitioners must resort to weak supervision. In addition to weaknesses of supervision, dataset shifts are another kind of phenomenon that occurs when deploying machine learning models in the real world. That is why Biquality Learning has been proposed as a machine learning framework to design algorithms capable of handling multiple weaknesses of supervision and dataset shifts without assumptions on their nature and level by relying on the availability of a small trusted dataset composed of cleanly labeled and representative samples. Thus we propose biquality-learn: a Python library for Biquality Learning with an intuitive and consistent API to learn machine learning models from biquality data, with well-proven algorithms, accessible and easy to use for everyone, and enabling researchers to experiment in a reproducible way on biquality data.