# Extreme Multi-label Classification from Aggregated Labels

arXiv: https://arxiv.org/abs/2004.00198 · HTML: https://mlsanity.com/paper/2004.00198/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction](https://mlsanity.com/paper/2111.00064/) (2021)
- [SCR: Training Graph Neural Networks with Consistency Regularization](https://mlsanity.com/paper/2112.04319/) (2021)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [Priberam at MESINESP Multi-label Classification of Medical Texts Task](https://mlsanity.com/paper/2105.05614/) (2021)
- [Private Multi-Winner Voting for Machine Learning](https://mlsanity.com/paper/2211.15410/) (2022)

## Abstract (arXiv)

Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes. We develop a new and scalable algorithm to impute individual-sample labels from the group labels; this can be paired with any existing XMC method to solve the aggregated label problem. We characterize the statistical properties of our algorithm under mild assumptions, and provide a new end-to-end framework for MIML as an extension. Experiments on both aggregated label XMC and MIML tasks show the advantages over existing approaches.