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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Balanced Self-Paced Learning for AUC Maximization

February 1, 2023

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Authors

Bin Gu

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE Nanjing University of Information Science & Technology, Nanjing, China


Chenkang Zhang

Nanjing University of Information Science & Technology, Nanjing, China


Huan Xiong

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE Institute for Advanced Study in Mathematics, Harbin Institute of Technology, China


Heng Huang

University of Pittsburgh, Pittsburgh, USA


DOI:

10.1609/aaai.v36i6.20632


Abstract:

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.

Topics: AAAI

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HOW TO CITE:

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang Balanced Self-Paced Learning for AUC Maximization Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang Balanced Self-Paced Learning for AUC Maximization AAAI 2022, 6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang (2022). Balanced Self-Paced Learning for AUC Maximization. Proceedings of the AAAI Conference on Artificial Intelligence, 6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. Balanced Self-Paced Learning for AUC Maximization. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. 2022. Balanced Self-Paced Learning for AUC Maximization. "Proceedings of the AAAI Conference on Artificial Intelligence". 6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. (2022) "Balanced Self-Paced Learning for AUC Maximization", Proceedings of the AAAI Conference on Artificial Intelligence, p.6765-6773

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang, "Balanced Self-Paced Learning for AUC Maximization", AAAI, p.6765-6773, 2022.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. "Balanced Self-Paced Learning for AUC Maximization". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. "Balanced Self-Paced Learning for AUC Maximization". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6765-6773.

Bin Gu||Chenkang Zhang||Huan Xiong||Heng Huang. Balanced Self-Paced Learning for AUC Maximization. AAAI[Internet]. 2022[cited 2023]; 6765-6773.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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