# Two-Way Partial AUC and Its Properties

arXiv: https://arxiv.org/abs/1508.00298 · HTML: https://mlsanity.com/paper/1508.00298/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2015.

## Built on by (most cited first, 11 of 11)

- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee](https://mlsanity.com/paper/2203.00176/) (2022)
- [Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation](https://mlsanity.com/paper/2103.11357/) (2021)
- [Large-scale Optimization of Partial AUC in a Range of False Positive Rates](https://mlsanity.com/paper/2203.01505/) (2022)
- [Optimizing Two-way Partial AUC with an End-to-end Framework](https://mlsanity.com/paper/2206.11655/) (2022)
- [Weakly Supervised AUC Optimization: A Unified Partial AUC Approach](https://mlsanity.com/paper/2305.14258/) (2023)
- [Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation](https://mlsanity.com/paper/2403.00844/) (2024)
- [Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm](https://mlsanity.com/paper/2210.03967/) (2022)
- [Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization](https://mlsanity.com/paper/2505.21944/) (2025)
- [FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport](https://mlsanity.com/paper/2508.03940/) (2025)
- [Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations](https://mlsanity.com/paper/2512.01213/) (2025)

## Abstract (arXiv)

When people evaluate the performance of a diagnostic test, it is important to control both True Positive Rate (TPR) and False Positive Rate (FPR). In the literature, most researchers propose the partial area under the ROC curve (pAUC) with restrictions on FPR to assess a binary classification system, which is named as FPR pAUC. It could be artificially designed to measure the area controlled by TPR and FPR, but is often misleading conceptually and practically. A new and intuitive method, named two-way pAUC, is provided in this paper, which focuses directly on the partial area under the ROC curve with both horizontal and vertical restrictions. We propose a nonparametric estimator of two-way pAUC, obtain its asymptotic normality properties and conduct the measure comparison by bootstrap method. Further, in order to evaluate possible covariate effects on two-way pAUC, regression analysis framework is constructed and corresponding theoretical properties are established. Simulation and real application are conducted to support our methods.