# Stochastic Proximal AUC Maximization

arXiv: https://arxiv.org/abs/1906.06053 · HTML: https://mlsanity.com/paper/1906.06053/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [Stability and Generalization of Stochastic Gradient Methods for Minimax Problems](https://mlsanity.com/paper/2105.03793/) (2021)
- [Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning](https://mlsanity.com/paper/2111.12050/) (2021)
- [Minimax AUC Fairness: Efficient Algorithm with Provable Convergence](https://mlsanity.com/paper/2208.10451/) (2022)
- [Weakly Supervised AUC Optimization: A Unified Partial AUC Approach](https://mlsanity.com/paper/2305.14258/) (2023)
- [An Efficient and Robust System for Vertically Federated Random Forest](https://mlsanity.com/paper/2201.10761/) (2022)
- [Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms](https://mlsanity.com/paper/2307.03357/) (2023)
- [Stability and Generalization of Differentially Private Minimax Problems](https://mlsanity.com/paper/2204.04858/) (2022)
- [Enhancing Personalized Ranking With Differentiable Group AUC Optimization](https://mlsanity.com/paper/2304.09176/) (2023)
- [Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning](https://mlsanity.com/paper/2305.19640/) (2023)
- [Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC](https://mlsanity.com/paper/2110.09651/) (2021)
- [Learning Rates for Nonconvex Pairwise Learning](https://mlsanity.com/paper/2111.05232/) (2021)
- [Does it pay to optimize AUC?](https://mlsanity.com/paper/2306.01528/) (2023)
- [Online AUC Optimization Based on Second-order Surrogate Loss](https://mlsanity.com/paper/2510.21202/) (2025)

## Abstract (arXiv)

In this paper we consider the problem of maximizing the Area under the ROC curve (AUC) which is a widely used performance metric in imbalanced classification and anomaly detection. Due to the pairwise nonlinearity of the objective function, classical SGD algorithms do not apply to the task of AUC maximization. We propose a novel stochastic proximal algorithm for AUC maximization which is scalable to large scale streaming data. Our algorithm can accommodate general penalty terms and is easy to implement with favorable O(d) space and per-iteration time complexities. We establish a high-probability convergence rate O(1/√T) for the general convex setting, and improve it to a fast convergence rate O(1/T) for the cases of strongly convex regularizers and no regularization term (without strong convexity). Our proof does not need the uniform boundedness assumption on the loss function or the iterates which is more fidelity to the practice. Finally, we perform extensive experiments over various benchmark data sets from real-world application domains which show the superior performance of our algorithm over the existing AUC maximization algorithms.