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The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning.
Surfaces represented by the difference of convex functions
AD Alexandroff · 1950
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On functions representable as a difference of convex functions
Philip Hartman · 1959
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Minimizing the difference of two convex functions. I. Algorithms based on exact regularization
D. Gabay · 1982
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The meaning and use of the area under a receiver operating characteristic (roc) curve
James A Hanley and Barbara J McNeil · 1982
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Contribution à l’analyse et l’optimisation de différence de fonctions convexes
Rachid Ellaia · 1984
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Generalized differentiability/duality and optimization for problems dealing with differences of convex functions
J-B Hiriart-Urruty · 1985
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Analyzing a portion of the roc curve
Donna Katzman McClish · 1989
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On the statistical analysis of roc curves
Mary Lou Thompson and Walter Zucchini · 1989
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How to regularize a difference of convex functions
J-B Hiriart-Urruty · 1991
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Principles of risk minimization for learning theory
Vladimir Vapnik · 1992
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Dc optimization: theory, methods and algorithms
Hoang Tuy · 1995
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A receiver operating characteristic partial area index for highly sensitive diagnostic tests
Yulei Jiang, Charles E Metz, and Robert M Nishikawa · 1996
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The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P Bradley · 1997
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Convex analysis approach to dc programming: theory, algorithms and applications
Pham Dinh Tao and Le Thi Hoai An · 1997
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A dc optimization algorithm for solving the trust-region subproblem
Pham Dinh Tao and Le Thi Hoai An · 1998
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Partial auc estimation and regression
Lori E Dodd and Margaret S Pepe · 2003
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Proximal point algorithm for minimization of dc function
Wen-yu Sun, Raimundo JB Sampaio, and MAB Candido · 2003
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The concave-convex procedure
Alan L Yuille and Anand Rangarajan · 2003
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The dc (difference of convex functions) programming and dca revisited with dc models of real world nonconvex optimization problems
Le Thi Hoai An and Pham Dinh Tao · 2005
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On the convergence of an approximate proximal method for dc functions
Abdellatif Moudafi and Paul-Emile Maingé · 2006
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On the difference of two maximal monotone operators: Regularization and algorithmic approaches
Abdellatif Moudafi · 2008
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Variational analysis , volume 317
R Tyrrell Rockafellar and Roger J-B Wets · 2009
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On the convergence of the concave-convex procedure
Bharath K Sriperumbudur and Gert RG Lanckriet · 2009
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A boosting method for maximizing the partial area under the roc curve
Osamu Komori and Shinto Eguchi · 2010
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Libsvm: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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Partial auc maximization in a linear combination of dichotomizers
Maria Teresa Ricamato and Francesco Tortorella · 2011
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Marker selection via maximizing the partial area under the roc curve of linear risk scores
Zhanfeng Wang and Yuan-Chin Ivan Chang · 2011
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On use of partial area under the roc curve for evaluation of diagnostic performance
Hua Ma, Andriy I Bandos, Howard E Rockette, and David Gur · 2013
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Stochastic majorization-minimization algorithms for large-scale optimization
Partial auc maximization via nonlinear scoring functions
Naonori Ueda and Akinori Fujino · 2018
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A proximal difference-of-convex algorithm with extrapolation
Bo Wen, Xiaojun Chen, and Ting Kei Pong · 2018
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Stochastic difference-of-convex algorithms for solving nonconvex optimization problems
Le Thi Hoai An, Huynh Van Ngai, Pham Dinh Tao, and Luu Hoang Phuc Hau · 2019
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Proximally guided stochastic subgradient method for nonsmooth, nonconvex problems
Damek Davis and Benjamin Grimmer · 2019
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Efficiency of minimizing compositions of convex functions and smooth maps
Dmitriy Drusvyatskiy and Courtney Paquette · 2019
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Julien Mairal · 2013
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Half-auc for the evaluation of sensitive or specific classifiers
Andrew P Bradley · 2014
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Online and stochastic gradient methods for non-decomposable loss functions
Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain · 2014
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Variations and extension of the convex–concave procedure
Thomas Lipp and Stephen Boyd · 2016
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Minimizing the maximal loss: How and why
Shai Shalev-Shwartz and Yonatan Wexler · 2016
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Global convergence of a proximal linearized algorithm for difference of convex functions
João Carlos O Souza, Paulo Roberto Oliveira, and Antoine Soubeyran · 2016
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Stochastic online auc maximization
Yiming Ying, Longyin Wen, and Siwei Lyu · 2016
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, 2019
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
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Stochastic optimization for dc functions and non-smooth non-convex regularizers with non-asymptotic convergence
Yi Xu, Qi Qi, Qihang Lin, Rong Jin, and Tianbao Yang · 2019
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Two-way partial auc and its properties
Hanfang Yang, Kun Lu, Xiang Lyu, and Feifang Hu · 2019
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Efficiency of coordinate descent methods for structured nonconvex optimization
Qi Deng and Chenghao Lan · 2020
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Learning by minimizing the sum of ranked range
Shu Hu, Yiming Ying, Siwei Lyu, et al · 2020
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Quadratically regularized subgradient methods for weakly convex optimization with weakly convex constraints
Runchao Ma, Qihang Lin, and Tianbao Yang · 2020
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Zhuoning Yuan, Yan Yan, Milan Sonka, and Tianbao Yang · 2020
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On the rate of convergence of the difference-of-convex algorithm (dca)
Hadi Abbaszadehpeivasti, Etienne de Klerk, and Moslem Zamani · 2021
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Accelerated proximal stochastic variance reduction for dc optimization
Lulu He, Jimin Ye, et al · 2021
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First-order convergence theory for weakly-convex-weakly-concave min-max problems
Mingrui Liu, Hassan Rafique, Qihang Lin, and Tianbao Yang · 2021
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A complete smooth regularization of dc optimization problems
Abdellatif Moudafi · 2021
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Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2021
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Algorithms for difference-of-convex (dc) programs based on difference-of-moreau-envelopes smoothing
Kaizhao Sun and Xu Andy Sun · 2021
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Online stochastic dca with applications to principal component analysis
Hoai An Le Thi, Hoang Phuc Hau Luu, and Tao Pham Dinh · 2021
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Large-scale robust deep AUC maximization: A new surrogate loss and empirical studies on medical image classification
Zhuoning Yuan, Yan Yan, Milan Sonka, and Tianbao Yang · 2021
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Auc maximization in the era of big data and ai: A survey
Tianbao Yang and Yiming Ying · 2022
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Optimizing two-way partial auc with an end-to-end framework
Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, and Qingming Huang · 2022
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When auc meets dro: Optimizing partial auc for deep learning with non-convex convergence guarantee
Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, and Tianbao Yang · 2022
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