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   Zhu, Dixian and ( July 2022 , Proceedings of the 39th International Conference on Machine Learning)

   Chaudhuri, Kamalika and (Ed.)

   In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning. We propose new formulations of pAUC surrogate objectives by using the distributionally robust optimization (DRO) to define the loss for each individual positive data. We consider two formulations of DRO, one of which is based on conditional-value-at-risk (CVaR) that yields a non-smooth but exact estimator for pAUC, and another one is based on a KL divergence regularized DRO that yields an inexact but smooth (soft) estimator for pAUC. For both one-way and two-way pAUC maximization, we propose two algorithms and prove their convergence for optimizing their two formulations, respectively. Experiments demonstrate the effectiveness of the proposed algorithms for pAUC maximization for deep learning on various datasets. 

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2. Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints

   Yao, Y. ; Lin, Q. ; Yang, T. ( January 2023 , The 26th International Conference on Artificial Intelligence and Statistics (AISTATS))
3. Quantifying relative virulence: when μ max fails and AUC alone just is not enough

   https://doi.org/10.1099/jgv.0.001515  

   Ceballos, Ruben Michael ; Stacy, Carson Len ( January 2021 , Journal of General Virology)

   null (Ed.)

   A challenge in virology is quantifying relative virulence ( V R ) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus–host interactions and to quantify the magnitude of detriment to host due to viral infection. Quantifying V R using canonical parameters, like maximum specific growth rate ( μ max ), can fail to provide reliable information regarding virulence. Although area-under-the-curve (AUC) calculations are more robust, they are sensitive to limit selection. Using empirical data from Sulfolobus Spindle-shaped Virus (SSV) infections, we introduce a novel, simple metric that has proven to be more robust than existing methods for assessing V R . This metric ( I SC ) accurately aligns biological phenomena with quantified metrics to determine V R . It also addresses a gap in virology by permitting comparisons between different non-lytic virus infections or non-lytic versus lytic virus infections on a given host in single-virus/single-host infections. 

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4. Stochastic auc maximization with deep neural networks

   Liu, Mingrui ; Yuan, Zhuoning ; Ying, Yiming ; Yang, Tianbao ( April 2020 , International Conference on Learning Representations (ICLR))
5. Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

   Hu, Q. ; Zhong, Y. ; Yang, T. ( January 2022 , Neural Information Processing Systems)