# MBA: Mini-Batch AUC Optimization

arXiv: https://arxiv.org/abs/1805.11221 · HTML: https://mlsanity.com/paper/1805.11221/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 7 of 7)

- [Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes](https://mlsanity.com/paper/2102.12894/) (2021)
- [Improved Diversity-Promoting Collaborative Metric Learning for Recommendation](https://mlsanity.com/paper/2409.01012/) (2024)
- [Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities](https://mlsanity.com/paper/2105.01545/) (2021)
- [Enhancing Personalized Ranking With Differentiable Group AUC Optimization](https://mlsanity.com/paper/2304.09176/) (2023)
- [Evolutionary Multitasking AUC Optimization](https://mlsanity.com/paper/2201.01145/) (2022)
- [Does it pay to optimize AUC?](https://mlsanity.com/paper/2306.01528/) (2023)
- [Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach](https://mlsanity.com/paper/2509.15573/) (2025)

## Abstract (arXiv)

Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for optimizing AUC have recently been proposed. However, handling very large datasets remains an open challenge for this problem. This paper proposes a novel approach to AUC maximization, based on sampling mini-batches of positive/negative instance pairs and computing U-statistics to approximate a global risk minimization problem. The resulting algorithm is simple, fast, and learning-rate free. We show that the number of samples required for good performance is independent of the number of pairs available, which is a quadratic function of the positive and negative instances. Extensive experiments show the practical utility of the proposed method.