{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:40:10Z","timestamp":1770352810502,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":75,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076138"],"award-info":[{"award-number":["62076138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599316","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"3195-3205","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Doubly Robust AUC Optimization against Noisy and Adversarial Samples"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0918-518X","authenticated-orcid":false,"given":"Chenkang","family":"Zhang","sequence":"first","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4895-4060","authenticated-orcid":false,"given":"Wanli","family":"Shi","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9976-0442","authenticated-orcid":false,"given":"Lei","family":"Luo","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8653-1117","authenticated-orcid":false,"given":"Bin","family":"Gu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology & Mohamed bin Zayed University of Artificial Intelligence, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110120"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09854-1"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Wieland Brendel Jonas Rauber Alexey Kurakin Nicolas Papernot Behar Veliqi Sharada P Mohanty Florian Laurent Marcel Salath\u00e9 Matthias Bethge Yaodong Yu etal 2020. Adversarial vision challenge. In The NeurIPS'18 Competition. Springer 129--153.  Wieland Brendel Jonas Rauber Alexey Kurakin Nicolas Papernot Behar Veliqi Sharada P Mohanty Florian Laurent Marcel Salath\u00e9 Matthias Bethge Yaodong Yu et al. 2020. Adversarial vision challenge. In The NeurIPS'18 Competition. Springer 129--153.","DOI":"10.1007\/978-3-030-29135-8_5"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2020.02.44"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/520"},{"key":"e_1_3_2_2_6_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Carmon Yair","year":"2019","unstructured":"Yair Carmon , Aditi Raghunathan , Ludwig Schmidt , John C Duchi , and Percy S Liang . 2019 . Unlabeled data improves adversarial robustness . Advances in Neural Information Processing Systems , Vol. 32 (2019). Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang. 2019. Unlabeled data improves adversarial robustness. Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01446"},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, 2206--2216","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce and Matthias Hein . 2020 . Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks . In International conference on machine learning. PMLR, 2206--2216 . Francesco Croce and Matthias Hein. 2020. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning. PMLR, 2206--2216."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3024987"},{"key":"e_1_3_2_2_10_1","volume-title":"Classification in the presence of label noise: a survey","author":"Michel Verleysen Fr\u00e9nay","year":"2013","unstructured":"Beno^it Fr\u00e9nay and Michel Verleysen . 2013. Classification in the presence of label noise: a survey . IEEE transactions on neural networks and learning systems, Vol. 25 , 5 ( 2013 ), 845--869. Beno^it Fr\u00e9nay and Michel Verleysen. 2013. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, Vol. 25, 5 (2013), 845--869."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/02331934.2010.522710"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.3934\/bdia.2016.1.111"},{"key":"e_1_3_2_2_14_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 ( 2014 ). Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013697"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00025"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Bin Gu Chenkang Zhang Huan Xiong and Heng Huang. 2022. Balanced Self-Paced Learning for AUC Maximization. (2022).  Bin Gu Chenkang Zhang Huan Xiong and Heng Huang. 2022. Balanced Self-Paced Learning for AUC Maximization. (2022).","DOI":"10.1609\/aaai.v36i6.20632"},{"key":"e_1_3_2_2_18_1","volume-title":"Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo , Mayank Rana , Moustapha Cisse , and Laurens Van Der Maaten . 2017. Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 ( 2017 ). Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten. 2017. Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)."},{"key":"e_1_3_2_2_19_1","volume-title":"Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine","author":"Hajian-Tilaki Karimollah","year":"2013","unstructured":"Karimollah Hajian-Tilaki . 2013. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine , Vol. 4 , 2 ( 2013 ), 627. Karimollah Hajian-Tilaki. 2013. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine , Vol. 4, 2 (2013), 627."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/IRI49571.2020.00022"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-019-00766-x"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512178"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1590\/S0101-82052003000100003"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5846"},{"key":"e_1_3_2_2_25_1","volume-title":"International Conference on Machine Learning. PMLR, 2304--2313","author":"Jiang Lu","year":"2018","unstructured":"Lu Jiang , Zhengyuan Zhou , Thomas Leung , Li-Jia Li , and Li Fei-Fei . 2018 . Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels . In International Conference on Machine Learning. PMLR, 2304--2313 . Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. 2018. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning. PMLR, 2304--2313."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.12.079"},{"key":"e_1_3_2_2_27_1","volume-title":"Conference on Robot Learning. PMLR, 513--529","author":"Klink Pascal","year":"2020","unstructured":"Pascal Klink , Hany Abdulsamad , Boris Belousov , and Jan Peters . 2020 . Self-paced contextual reinforcement learning . In Conference on Robot Learning. PMLR, 513--529 . Pascal Klink, Hany Abdulsamad, Boris Belousov, and Jan Peters. 2020. Self-paced contextual reinforcement learning. In Conference on Robot Learning. PMLR, 513--529."},{"key":"e_1_3_2_2_28_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_2_29_1","volume-title":"Self-paced learning for latent variable models. Advances in neural information processing systems","author":"Kumar M","year":"2010","unstructured":"M Kumar , Benjamin Packer , and Daphne Koller . 2010. Self-paced learning for latent variable models. Advances in neural information processing systems , Vol. 23 ( 2010 ). M Kumar, Benjamin Packer, and Daphne Koller. 2010. Self-paced learning for latent variable models. Advances in neural information processing systems , Vol. 23 (2010)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_31_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Li Chao","year":"2019","unstructured":"Chao Li , Shangqian Gao , Cheng Deng , De Xie , and Wei Liu . 2019 . Cross-modal learning with adversarial samples . Advances in Neural Information Processing Systems , Vol. 32 (2019). Chao Li, Shangqian Gao, Cheng Deng, De Xie, and Wei Liu. 2019. Cross-modal learning with adversarial samples. Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3447841"},{"key":"e_1_3_2_2_34_1","volume-title":"Understanding self-paced learning under co ve conjugacy theory. arXiv preprint arXiv:1805.08096","author":"Liu Shiqi","year":"2018","unstructured":"Shiqi Liu , Zilu Ma , and Deyu Meng . 2018. Understanding self-paced learning under co ve conjugacy theory. arXiv preprint arXiv:1805.08096 ( 2018 ). Shiqi Liu, Zilu Ma, and Deyu Meng. 2018. Understanding self-paced learning under co ve conjugacy theory. arXiv preprint arXiv:1805.08096 (2018)."},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Machine Learning. PMLR, 2275--2284","author":"Ma Fan","year":"2017","unstructured":"Fan Ma , Deyu Meng , Qi Xie , Zina Li , and Xuanyi Dong . 2017 . Self-paced co-training . In International Conference on Machine Learning. PMLR, 2275--2284 . Fan Ma, Deyu Meng, Qi Xie, Zina Li, and Xuanyi Dong. 2017. Self-paced co-training. In International Conference on Machine Learning. PMLR, 2275--2284."},{"key":"e_1_3_2_2_36_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Ma Xingjun","year":"2018","unstructured":"Xingjun Ma , Bo Li , Yisen Wang , Sarah M Erfani , Sudanthi Wijewickrema , Grant Schoenebeck , Dawn Song , Michael E Houle , and James Bailey . 2018 . Characterizing adversarial subspaces using local intrinsic dimensionality . In 6th International Conference on Learning Representations, ICLR 2019. Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey. 2018. Characterizing adversarial subspaces using local intrinsic dimensionality. In 6th International Conference on Learning Representations, ICLR 2019."},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2018 . Towards Deep Learning Models Resistant to Adversarial Attacks . In International Conference on Learning Representations. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_38_1","volume-title":"The Corsini encyclopedia of psychology","author":"McKnight Patrick E","year":"2010","unstructured":"Patrick E McKnight and Julius Najab . 2010. Mann-Whitney U Test . The Corsini encyclopedia of psychology ( 2010 ), 1--1. Patrick E McKnight and Julius Najab. 2010. Mann-Whitney U Test. The Corsini encyclopedia of psychology (2010), 1--1."},{"key":"e_1_3_2_2_39_1","first-page":"220","article-title":"Understanding auc-roc curve","volume":"26","author":"Narkhede Sarang","year":"2018","unstructured":"Sarang Narkhede . 2018 . Understanding auc-roc curve . Towards Data Science , Vol. 26 , 1 (2018), 220 -- 227 . Sarang Narkhede. 2018. Understanding auc-roc curve. Towards Data Science, Vol. 26, 1 (2018), 220--227.","journal-title":"Towards Data Science"},{"key":"e_1_3_2_2_40_1","unstructured":"Yuval Netzer Tao Wang Adam Coates Alessandro Bissacco Bo Wu and Andrew Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. (2011).  Yuval Netzer Tao Wang Adam Coates Alessandro Bissacco Bo Wu and Andrew Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. (2011)."},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Machine Learning. PMLR, 4970--4979","author":"Pang Tianyu","year":"2019","unstructured":"Tianyu Pang , Kun Xu , Chao Du , Ning Chen , and Jun Zhu . 2019 . Improving adversarial robustness via promoting ensemble diversity . In International Conference on Machine Learning. PMLR, 4970--4979 . Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. 2019. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning. PMLR, 4970--4979."},{"key":"e_1_3_2_2_42_1","volume-title":"Bag of Tricks for Adversarial Training. In International Conference on Learning Representations.","author":"Pang Tianyu","year":"2020","unstructured":"Tianyu Pang , Xiao Yang , Yinpeng Dong , Hang Su , and Jun Zhu . 2020 . Bag of Tricks for Adversarial Training. In International Conference on Learning Representations. Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu. 2020. Bag of Tricks for Adversarial Training. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Machine Learning. PMLR, 8093--8104","author":"Rice Leslie","year":"2020","unstructured":"Leslie Rice , Eric Wong , and Zico Kolter . 2020 . Overfitting in adversarially robust deep learning . In International Conference on Machine Learning. PMLR, 8093--8104 . Leslie Rice, Eric Wong, and Zico Kolter. 2020. Overfitting in adversarially robust deep learning. In International Conference on Machine Learning. PMLR, 8093--8104."},{"key":"e_1_3_2_2_45_1","volume-title":"Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein.","author":"Shafahi Ali","year":"2019","unstructured":"Ali Shafahi , Mahyar Najibi , Mohammad Amin Ghiasi , Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 . Adversarial training for free! Advances in Neural Information Processing Systems , Vol. 32 (2019). Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019. Adversarial training for free! Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_46_1","volume-title":"Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204","author":"Tram\u00e8r Florian","year":"2017","unstructured":"Florian Tram\u00e8r , Alexey Kurakin , Nicolas Papernot , Ian Goodfellow , Dan Boneh , and Patrick McDaniel . 2017. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204 ( 2017 ). Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2017. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204 (2017)."},{"key":"e_1_3_2_2_47_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Vahdat Arash","year":"2017","unstructured":"Arash Vahdat . 2017 . Toward robustness against label noise in training deep discriminative neural networks . Advances in Neural Information Processing Systems , Vol. 30 (2017). Arash Vahdat. 2017. Toward robustness against label noise in training deep discriminative neural networks. Advances in Neural Information Processing Systems , Vol. 30 (2017)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633979"},{"key":"e_1_3_2_2_49_1","volume-title":"Self-Paced Learning for Neural Machine Translation. arXiv preprint arXiv:2010.04505","author":"Wan Yu","year":"2020","unstructured":"Yu Wan , Baosong Yang , Derek F Wong , Yikai Zhou , Lidia S Chao , Haibo Zhang , and Boxing Chen . 2020. Self-Paced Learning for Neural Machine Translation. arXiv preprint arXiv:2010.04505 ( 2020 ). Yu Wan, Baosong Yang, Derek F Wong, Yikai Zhou, Lidia S Chao, Haibo Zhang, and Boxing Chen. 2020. Self-Paced Learning for Neural Machine Translation. arXiv preprint arXiv:2010.04505 (2020)."},{"key":"e_1_3_2_2_50_1","volume-title":"International Conference on Learning Representations.","author":"Wang Huaxia","year":"2018","unstructured":"Huaxia Wang and Chun-Nam Yu . 2018 . A Direct Approach to Robust Deep Learning Using Adversarial Networks . In International Conference on Learning Representations. Huaxia Wang and Chun-Nam Yu. 2018. A Direct Approach to Robust Deep Learning Using Adversarial Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_51_1","volume-title":"Marius George Linguraru, and Ronald M Summers","author":"Wang Shijun","year":"2015","unstructured":"Shijun Wang , Diana Li , Nicholas Petrick , Berkman Sahiner , Marius George Linguraru, and Ronald M Summers . 2015 . Optimizing area under the ROC curve using semi-supervised learning. Pattern recognition, Vol. 48 , 1 (2015), 276--287. Shijun Wang, Diana Li, Nicholas Petrick, Berkman Sahiner, Marius George Linguraru, and Ronald M Summers. 2015. Optimizing area under the ROC curve using semi-supervised learning. Pattern recognition, Vol. 48, 1 (2015), 276--287."},{"key":"e_1_3_2_2_52_1","volume-title":"IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters. arXiv preprint arXiv:1903.12141","author":"Wang Xinshao","year":"2019","unstructured":"Xinshao Wang , Yang Hua , Elyor Kodirov , and Neil M Robertson . 2019a. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters. arXiv preprint arXiv:1903.12141 ( 2019 ). Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson. 2019a. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters. arXiv preprint arXiv:1903.12141 (2019)."},{"key":"e_1_3_2_2_53_1","volume-title":"ICML 2019: Proceedings of the 36th International Conference on Machine Learning. PMLR, 11426--11438","author":"Wang Yisen","year":"2019","unstructured":"Yisen Wang , Xingjun Ma , James Bailey , Jinfeng Yi , Bowen Zhou , and Quanquan Gu . 2019 b. On the convergence and robustness of adversarial training . In ICML 2019: Proceedings of the 36th International Conference on Machine Learning. PMLR, 11426--11438 . Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu. 2019b. On the convergence and robustness of adversarial training. In ICML 2019: Proceedings of the 36th International Conference on Machine Learning. PMLR, 11426--11438."},{"key":"e_1_3_2_2_54_1","volume-title":"On the convergence and robustness of adversarial training. arXiv preprint arXiv:2112.08304","author":"Wang Yisen","year":"2021","unstructured":"Yisen Wang , Xingjun Ma , James Bailey , Jinfeng Yi , Bowen Zhou , and Quanquan Gu. 2021. On the convergence and robustness of adversarial training. arXiv preprint arXiv:2112.08304 ( 2021 ). Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu. 2021. On the convergence and robustness of adversarial training. arXiv preprint arXiv:2112.08304 (2021)."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac112"},{"key":"e_1_3_2_2_57_1","volume-title":"Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994","author":"Wong Eric","year":"2020","unstructured":"Eric Wong , Leslie Rice , and J Zico Kolter . 2020. Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994 ( 2020 ). Eric Wong, Leslie Rice, and J Zico Kolter. 2020. Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994 (2020)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17237"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00064"},{"key":"e_1_3_2_2_60_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 2691--2699","author":"Xiao Tong","year":"2015","unstructured":"Tong Xiao , Tian Xia , Yi Yang , Chang Huang , and Xiaogang Wang . 2015 . Learning from massive noisy labeled data for image classification . In Proceedings of the IEEE conference on computer vision and pattern recognition. 2691--2699 . Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. 2015. Learning from massive noisy labeled data for image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2691--2699."},{"key":"e_1_3_2_2_61_1","volume-title":"Smooth adversarial training. arXiv preprint arXiv:2006.14536","author":"Xie Cihang","year":"2020","unstructured":"Cihang Xie , Mingxing Tan , Boqing Gong , Alan Yuille , and Quoc V Le. 2020. Smooth adversarial training. arXiv preprint arXiv:2006.14536 ( 2020 ). Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, and Quoc V Le. 2020. Smooth adversarial training. arXiv preprint arXiv:2006.14536 (2020)."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539307"},{"key":"e_1_3_2_2_63_1","volume-title":"Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155","author":"Xu Weilin","year":"2017","unstructured":"Weilin Xu , David Evans , and Yanjun Qi. 2017. Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 ( 2017 ). Weilin Xu, David Evans, and Yanjun Qi. 2017. Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 (2017)."},{"key":"e_1_3_2_2_64_1","volume-title":"A novel information-theoretic loss function for training deep nets robust to label noise. Advances in neural information processing systems","author":"Xu Yilun","year":"2019","unstructured":"Yilun Xu , Peng Cao , Yuqing Kong , and Yizhou Wang . 2019. L_dmi : A novel information-theoretic loss function for training deep nets robust to label noise. Advances in neural information processing systems , Vol. 32 ( 2019 ). Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang. 2019. L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise. Advances in neural information processing systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_65_1","volume-title":"Stochastic online auc maximization. Advances in neural information processing systems","author":"Ying Yiming","year":"2016","unstructured":"Yiming Ying , Longyin Wen , and Siwei Lyu . 2016. Stochastic online auc maximization. Advances in neural information processing systems , Vol. 29 ( 2016 ). Yiming Ying, Longyin Wen, and Siwei Lyu. 2016. Stochastic online auc maximization. Advances in neural information processing systems , Vol. 29 (2016)."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00303"},{"key":"e_1_3_2_2_67_1","volume-title":"Adversarially robust generalization just requires more unlabeled data. arXiv preprint arXiv:1906.00555","author":"Zhai Runtian","year":"2019","unstructured":"Runtian Zhai , Tianle Cai , Di He , Chen Dan , Kun He , John Hopcroft , and Liwei Wang . 2019. Adversarially robust generalization just requires more unlabeled data. arXiv preprint arXiv:1906.00555 ( 2019 ). Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang. 2019. Adversarially robust generalization just requires more unlabeled data. arXiv preprint arXiv:1906.00555 (2019)."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_2_69_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Zhang Dinghuai","year":"2019","unstructured":"Dinghuai Zhang , Tianyuan Zhang , Yiping Lu , Zhanxing Zhu , and Bin Dong . 2019 b. You only propagate once: Accelerating adversarial training via maximal principle . Advances in Neural Information Processing Systems , Vol. 32 (2019). Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, and Bin Dong. 2019b. You only propagate once: Accelerating adversarial training via maximal principle. Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_70_1","volume-title":"International Conference on Machine Learning. PMLR, 7472--7482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang , Yaodong Yu , Jiantao Jiao , Eric Xing , Laurent El Ghaoui , and Michael Jordan . 2019 a. Theoretically principled trade-off between robustness and accuracy . In International Conference on Machine Learning. PMLR, 7472--7482 . Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019a. Theoretically principled trade-off between robustness and accuracy. In International Conference on Machine Learning. PMLR, 7472--7482."},{"key":"e_1_3_2_2_71_1","volume-title":"International Conference on Machine Learning. PMLR, 11278--11287","author":"Zhang Jingfeng","year":"2020","unstructured":"Jingfeng Zhang , Xilie Xu , Bo Han , Gang Niu , Lizhen Cui , Masashi Sugiyama , and Mohan Kankanhalli . 2020 a. Attacks which do not kill training make adversarial learning stronger . In International Conference on Machine Learning. PMLR, 11278--11287 . Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli. 2020a. Attacks which do not kill training make adversarial learning stronger. In International Conference on Machine Learning. PMLR, 11278--11287."},{"key":"e_1_3_2_2_72_1","volume-title":"International conference on machine learning. PMLR, 11278--11287","author":"Zhang Jingfeng","year":"2020","unstructured":"Jingfeng Zhang , Xilie Xu , Bo Han , Gang Niu , Lizhen Cui , Masashi Sugiyama , and Mohan Kankanhalli . 2020 b. Attacks which do not kill training make adversarial learning stronger . In International conference on machine learning. PMLR, 11278--11287 . Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli. 2020b. Attacks which do not kill training make adversarial learning stronger. In International conference on machine learning. PMLR, 11278--11287."},{"key":"e_1_3_2_2_73_1","volume-title":"Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert Sabuncu . 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems , Vol. 31 ( 2018 ). Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems , Vol. 31 (2018)."},{"key":"e_1_3_2_2_74_1","volume-title":"Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055","author":"Zou Zhengxia","year":"2019","unstructured":"Zhengxia Zou , Zhenwei Shi , Yuhong Guo , and Jieping Ye. 2019. Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055 ( 2019 ). Zhengxia Zou, Zhenwei Shi, Yuhong Guo, and Jieping Ye. 2019. Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055 (2019)."},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijcsit.2012.4304"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599316","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:47Z","timestamp":1750178267000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":75,"alternative-id":["10.1145\/3580305.3599316","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599316","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}