# Balanced Self-Paced Learning for AUC Maximization

arXiv: https://arxiv.org/abs/2207.03650 · HTML: https://mlsanity.com/paper/2207.03650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 8 Jul 2022.

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

- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/1908.10831/) (2019)
- [One-Pass AUC Optimization](https://mlsanity.com/paper/1305.1363/) (2013)
- [Scalable Kernel Methods via Doubly Stochastic Gradients](https://mlsanity.com/paper/1407.5599/) (2014)
- [Robust Graph Learning from Noisy Data](https://mlsanity.com/paper/1812.06673/) (2018)
- [Self-Paced Learning for Neural Machine Translation](https://mlsanity.com/paper/2010.04505/) (2020)
- [Self-Paced Contextual Reinforcement Learning](https://mlsanity.com/paper/1910.02826/) (2019)
- [Safe Sample Screening for Robust Support Vector Machine](https://mlsanity.com/paper/1912.11217/) (2019)
- [Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization](https://mlsanity.com/paper/1810.02024/) (2018)
- [Understanding Self-Paced Learning under Concave Conjugacy Theory](https://mlsanity.com/paper/1805.08096/) (2018)

## Abstract (arXiv)

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods.