# Decontamination of Mutual Contamination Models

arXiv: https://arxiv.org/abs/1710.01167 · HTML: https://mlsanity.com/paper/1710.01167/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2110.12911/) (2021)
- [Unified Risk Analysis for Weakly Supervised Learning](https://mlsanity.com/paper/2309.08216/) (2023)
- [Beyond Black Box Densities: Parameter Learning for the Deviated Components](https://mlsanity.com/paper/2202.02651/) (2022)
- [Decompositional Generation Process for Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2204.03845/) (2022)
- [Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders](https://mlsanity.com/paper/2607.24921/) (2026)
- [Multiclass Classification without Labels via Posterior Simplex Geometry](https://mlsanity.com/paper/2607.24943/) (2026)
- [Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification](https://mlsanity.com/paper/2609.39829/) (2026)
- [Nous: Learning and Certifying Memory Decisions Before Source Calibration](https://mlsanity.com/paper/2610.00094/) (2026)

## Abstract (arXiv)

Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions and the goal is to infer these base distributions. This paper considers the general setting where the base distributions are defined on arbitrary probability spaces. We examine three popular machine learning problems that arise in this general setting: multiclass classification with label noise, demixing of mixed membership models, and classification with partial labels. In each case, we give sufficient conditions for identifiability and present algorithms for the infinite and finite sample settings, with associated performance guarantees.