Stochastic AUC optimization with general loss

Zhenhuan Yang;  ; Wei Shen; Yiming Ying; Xiaoming Yuan;  ;

doi:10.3934/cpaa.2020188

A Sparse Nonlinear Classifier Design Using AUC Optimization

Proceedings of the 2017 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974973.33 ◽

2017 ◽

pp. 291-299 ◽

Cited By ~ 2

Author(s):

Vishal Kakkar ◽

Shirish Shevade ◽

S Sundararajan ◽

Dinesh Garg

Keyword(s):

Classifier Design ◽

Auc Optimization ◽

Nonlinear Classifier

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AUC optimization based confidence measure for keyword spotting

10.21437/interspeech.2011-357 ◽

2011 ◽

Author(s):

Haiyang Li ◽

Jiqing Han ◽

Tieran Zheng

Keyword(s):

Keyword Spotting ◽

Confidence Measure ◽

Auc Optimization

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MBA: Mini-Batch AUC Optimization

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2020.2969527 ◽

2020 ◽

Vol 31 (12) ◽

pp. 5561-5574

Author(s):

San Gultekin ◽

Avishek Saha ◽

Adwait Ratnaparkhi ◽

John Paisley

Keyword(s):

Auc Optimization

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Learning Personalized Attribute Preference via Multi-Task AUC Optimization

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015660 ◽

2019 ◽

Vol 33 ◽

pp. 5660-5667

Author(s):

Zhiyong Yang ◽

Qianqian Xu ◽

Xiaochun Cao ◽

Qingming Huang

Keyword(s):

Wide Spectrum ◽

Closed Form Solution ◽

Form Solution ◽

Learning Methods ◽

Auc Optimization ◽

Area Under Roc Curve ◽

Multi Level ◽

Generalization Bound ◽

Attribute Learning ◽

Task Parameter

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute words are involved. In this paper, we develop a novel multi-task method to understand and predict personalized attribute annotations. Regarding the attribute preference learning for each annotator as a specific task, we first propose a multi-level task parameter decomposition to capture the evolution from a highly popular opinion of the mass to highly personalized choices that are special for each person. Meanwhile, for personalized learning methods, ranking prediction is much more important than accurate classification. This motivates us to employ an Area Under ROC Curve (AUC) based loss function to improve our model. On top of the AUC-based loss, we propose an efficient method to evaluate the loss and gradients. Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors. Furthermore, we also provide a generalization bound to guarantee a reasonable performance. Finally, empirical analysis consistently speaks to the efficacy of our proposed method.

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Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/399 ◽

2018 ◽

Cited By ~ 1

Author(s):

Zheng Xie ◽

Ming Li

Keyword(s):

Outcome Prediction ◽

Optimization Method ◽

Software System ◽

Continuous Integration ◽

Auc Optimization ◽

Code Changes

Continuous Integration (CI) systems aim to provide quick feedback on the success of the code changes by keeping on building the entire systems upon code changes are committed. However, building the entire software system is usually resource and time consuming. Thus, build outcome prediction is usually employed to distinguish the successful builds from the failed ones to cut the building efforts on those successful builds that do not result in any immediate action of the developer. Nevertheless, build outcome prediction in CI is challenging since the learner should be able to learn from a stream of build events with and without the build outcome labels and provide immediate prediction on the next build event. Also, the distribution of the successful and the failed builds are often highly imbalanced. Unfortunately, the existing methods fail to address these challenges well. In this paper, we address these challenges by proposing a semi-supervised online AUC optimization method for CI build outcome prediction. Experiments indicate that our method is able to cut the software building efforts by effectively identify the successful builds, and it outperforms the existing methods that elaborate to address part of these challenges.

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