Balanced Self-Paced Learning for AUC Maximization

by   Bin Gu, et al.

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.


page 1

page 2

page 3

page 4


Stochastic Proximal AUC Maximization

In this paper we consider the problem of maximizing the Area under the R...

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

In this paper, we study multi-block min-max bilevel optimization problem...

Robust AUC Optimization under the Supervision of Clean Data

AUC (area under the ROC curve) optimization algorithms have drawn much a...

Learning Personalized Attribute Preference via Multi-task AUC Optimization

Traditionally, most of the existing attribute learning methods are train...

Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization

Semi-supervised learning is pervasive in real-world applications, where ...

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

In this paper, we propose systematic and efficient gradient-based method...

Evolutionary Multitasking AUC Optimization

Learning to optimize the area under the receiver operating characteristi...

Please sign up or login with your details

Forgot password? Click here to reset