# Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach

arXiv: https://arxiv.org/abs/2401.11145 · HTML: https://mlsanity.com/paper/2401.11145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Masked Autoregressive Flow for Density Estimation](https://mlsanity.com/paper/1705.07057/) (2017)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Dynamical Variational Autoencoders: A Comprehensive Review](https://mlsanity.com/paper/2008.12595/) (2020)
- [Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning](https://mlsanity.com/paper/1603.03130/) (2016)
- [From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search](https://mlsanity.com/paper/2204.03096/) (2022)
- [Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search](https://mlsanity.com/paper/2212.09017/) (2022)
- [Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study](https://mlsanity.com/paper/2112.04090/) (2021)
- [Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning](https://mlsanity.com/paper/1910.13339/) (2019)

## Abstract (arXiv)

Document set expansion aims to identify relevant documents from a large collection based on a small set of documents that are on a fine-grained topic. Previous work shows that PU learning is a promising method for this task. However, some serious issues remain unresolved, i.e. typical challenges that PU methods suffer such as unknown class prior and imbalanced data, and the need for transductive experimental settings. In this paper, we propose a novel PU learning framework based on density estimation, called puDE, that can handle the above issues. The advantage of puDE is that it neither constrained to the SCAR assumption and nor require any class prior knowledge. We demonstrate the effectiveness of the proposed method using a series of real-world datasets and conclude that our method is a better alternative for the DSE task.