# On the Complexity of Learning from Label Proportions

arXiv: https://arxiv.org/abs/2004.03515 · HTML: https://mlsanity.com/paper/2004.03515/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions](https://mlsanity.com/paper/2401.11081/) (2024)
- [Learning under Label Proportions for Text Classification](https://mlsanity.com/paper/2310.11707/) (2023)
- [AI and Core Electoral Processes: Mapping the Horizons](https://mlsanity.com/paper/2302.03774/) (2023)
- [Learning Conditional Averages](https://mlsanity.com/paper/2602.11920/) (2026)

## Abstract (arXiv)

In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given. The goal is to learn a hypothesis that predicts the proportions of labels on the distribution underlying the sample. This model of learning is applicable to a wide variety of settings, including predicting the number of votes for candidates in political elections from polls. In this paper, we formally define this class and resolve foundational questions regarding the computational complexity of LLP and characterize its relationship to PAC learning. Among our results, we show, perhaps surprisingly, that for finite VC classes what can be efficiently LLP learned is a strict subset of what can be leaned efficiently in PAC, under standard complexity assumptions. We also show that there exist classes of functions whose learnability in LLP is independent of ZFC, the standard set theoretic axioms. This implies that LLP learning cannot be easily characterized (like PAC by VC dimension).