# Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning

arXiv: https://arxiv.org/abs/1705.01708 · HTML: https://mlsanity.com/paper/1705.01708/

## Key facts

- Cited by 19 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 May 2017.

## Built on by (most cited first, 19 of 19)

- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [Weakly Supervised AUC Optimization: A Unified Partial AUC Approach](https://mlsanity.com/paper/2305.14258/) (2023)
- [Class-Imbalanced Complementary-Label Learning via Weighted Loss](https://mlsanity.com/paper/2209.14189/) (2022)
- [Unified Risk Analysis for Weakly Supervised Learning](https://mlsanity.com/paper/2309.08216/) (2023)
- [PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision](https://mlsanity.com/paper/2407.06698/) (2024)
- [Labels, Information, and Computation: Efficient Learning Using Sufficient Labels](https://mlsanity.com/paper/2104.09015/) (2021)
- [Meta-learning for Positive-unlabeled Classification](https://mlsanity.com/paper/2406.03680/) (2024)
- [Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data](https://mlsanity.com/paper/2303.04797/) (2023)
- [Learning from True-False Labels via Multi-modal Prompt Retrieving](https://mlsanity.com/paper/2405.15228/) (2024)
- [A Symmetric Loss Perspective of Reliable Machine Learning](https://mlsanity.com/paper/2101.01366/) (2021)
- [Learning from Stochastic Labels](https://mlsanity.com/paper/2302.00299/) (2023)
- [AUC Optimization from Multiple Unlabeled Datasets](https://mlsanity.com/paper/2305.15776/) (2023)
- [A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision](https://mlsanity.com/paper/2507.07771/) (2025)
- [Needles in the Landscape: Semi-Supervised Pseudolabeling for Archaeological Site Discovery under Label Scarcity](https://mlsanity.com/paper/2510.16814/) (2025)
- [Graph-based Semi-Supervised Learning via Maximum Discrimination](https://mlsanity.com/paper/2602.08042/) (2026)
- [Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite](https://mlsanity.com/paper/2607.11947/) (2026)
- [Hit Selection Using SSMD-Based Machine Learning Performance Metrics in High-Throughput Screening Assays](https://mlsanity.com/paper/2608.07609/) (2026)
- [AUC Maximization from Biased Positive-unlabeled Data with Confidence](https://mlsanity.com/paper/2609.10928/) (2026)
- [Partial AUC Maximization from Positive-unlabeled Data](https://mlsanity.com/paper/2610.00284/) (2026)

## Abstract (arXiv)

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data (PU-AUC) and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.