{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T19:07:12Z","timestamp":1775588832640,"version":"3.50.1"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"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":"crossref","award":["62072257"],"award-info":[{"award-number":["62072257"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP22010371, LE220100078"],"award-info":[{"award-number":["DP22010371, LE220100078"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users\u2019 trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a\n            <jats:italic>Counterfactual Explanation for Fairness (CFairER)<\/jats:italic>\n            that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our\n            <jats:italic>CFairER<\/jats:italic>\n            conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.\n          <\/jats:p>","DOI":"10.1145\/3643670","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T12:39:47Z","timestamp":1706531987000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Counterfactual Explanation for Fairness in Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-3353","authenticated-orcid":false,"given":"Xiangmeng","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-9551","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth, WA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-9667","authenticated-orcid":false,"given":"Dianer","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia and The Education University of Hong Kong, Tai Po, Hong Kong Special Administrative Region of China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Jinzhen Zhang Qinghua Zhang Zhihua Ai and Xintai Li. 2021. Context-based user typicality collaborative filtering recommendation. Human-Centric Intelligent Systems 1 1-2 (2021) 43\u201353.","DOI":"10.2991\/hcis.k.210524.001"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Wen Huang Kevin Labille Xintao Wu Dongwon Lee and Neil Heffernan. 2022. Achieving user-side fairness in contextual bandits. Human-Centric Intelligent Systems 2 3-4 (2022) 81\u201394.","DOI":"10.1007\/s44230-022-00008-w"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_3_2_5_2","article-title":"Explainability for fair machine learning","author":"Begley Tom","year":"2020","unstructured":"Tom Begley, Tobias Schwedes, Christopher Frye, and Ilya Feige. 2020. Explainability for fair machine learning. arXiv preprint arXiv:2010.07389 (2020).","journal-title":"arXiv preprint arXiv:2010.07389"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371832"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330745"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/978-3-642-35289-8_25","volume-title":"Neural Networks: Tricks of the Trade","author":"Bottou L\u00e9on","year":"2012","unstructured":"L\u00e9on Bottou. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade. Springer, 421\u2013436."},{"key":"e_1_3_2_9_2","volume-title":"The Rational Imagination: How People Create Alternatives to Reality","author":"Byrne Ruth M. J.","year":"2007","unstructured":"Ruth M. J. Byrne. 2007. The Rational Imagination: How People Create Alternatives to Reality. MIT Press."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174225"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"issue":"5","key":"e_1_3_2_12_2","article-title":"Explaining recommender systems fairness and accuracy through the lens of data characteristics","volume":"58","author":"Deldjoo Yashar","year":"2021","unstructured":"Yashar Deldjoo, Alejandro Bellogin, and Tommaso Di Noia. 2021. Explaining recommender systems fairness and accuracy through the lens of data characteristics. Information Processing & Management 58, 5 (2021), 102662.","journal-title":"Information Processing & Management"},{"key":"e_1_3_2_13_2","first-page":"1597","volume-title":"2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)","author":"Dey Rahul","year":"2017","unstructured":"Rahul Dey and Fathi M. Salem. 2017. Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 1597\u20131600."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411962"},{"key":"e_1_3_2_15_2","first-page":"7441","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Dong Yushun","year":"2023","unstructured":"Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, and Jundong Li. 2023. Interpreting unfairness in graph neural networks via training node attribution. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 7441\u20137449."},{"key":"e_1_3_2_16_2","article-title":"Explaining data-driven decisions made by AI systems: The counterfactual approach","volume":"2001","author":"Fernandez Carlos","year":"2020","unstructured":"Carlos Fernandez, Foster J. Provost, and Xintian Han. 2020. Explaining data-driven decisions made by AI systems: The counterfactual approach. CoRR abs\/2001.07417 (2020). arXiv:2001.07417https:\/\/arxiv.org\/abs\/2001.07417","journal-title":"CoRR"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401051"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441824"},{"key":"e_1_3_2_19_2","article-title":"Counterfactual evaluation for explainable AI","author":"Ge Yingqiang","year":"2021","unstructured":"Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, and Yongfeng Zhang. 2021. Counterfactual evaluation for explainable AI. arXiv preprint arXiv:2109.01962 (2021).","journal-title":"arXiv preprint arXiv:2109.01962"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531973"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498487"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371824"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772731"},{"key":"e_1_3_2_24_2","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482327"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219965"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3213586.3226206"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533725"},{"key":"e_1_3_2_30_2","first-page":"1","article-title":"Deep treatment-adaptive network for causal inference","author":"Li Qian","year":"2022","unstructured":"Qian Li, Zhichao Wang, Shaowu Liu, Gang Li, and Guandong Xu. 2022. Deep treatment-adaptive network for causal inference. The VLDB Journal (2022), 1\u201316.","journal-title":"The VLDB Journal"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449866"},{"key":"e_1_3_2_32_2","article-title":"Fairness in recommendation: A survey","author":"Li Yunqi","year":"2022","unstructured":"Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022. Fairness in recommendation: A survey. arXiv preprint arXiv:2205.13619 (2022).","journal-title":"arXiv preprint arXiv:2205.13619"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462966"},{"key":"e_1_3_2_34_2","first-page":"155","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Liu Weiwen","year":"2020","unstructured":"Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, and Pheng Ann Heng. 2020. Balancing between accuracy and fairness for interactive recommendation with reinforcement learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 155\u2013167."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380130"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340631.3394860"},{"key":"e_1_3_2_37_2","first-page":"709","volume-title":"Asian Conference on Computer Vision","author":"Nguyen Hieu V.","year":"2010","unstructured":"Hieu V. Nguyen and Li Bai. 2010. Cosine similarity metric learning for face verification. In Asian Conference on Computer Vision. Springer, 709\u2013720."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568012"},{"issue":"98","key":"e_1_3_2_39_2","article-title":"The Matthew effect in empirical data","volume":"11","author":"Perc Matja\u017e","year":"2014","unstructured":"Matja\u017e Perc. 2014. The Matthew effect in empirical data. Journal of The Royal Society Interface 11, 98 (2014), 20140378.","journal-title":"Journal of The Royal Society Interface"},{"key":"e_1_3_2_40_2","article-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).","journal-title":"arXiv preprint arXiv:1205.2618"},{"key":"e_1_3_2_41_2","article-title":"Privacy and fairness in recommender systems via adversarial training of user representations","author":"Resheff Yehezkel S.","year":"2018","unstructured":"Yehezkel S. Resheff, Yanai Elazar, Moni Shahar, and Oren Sar Shalom. 2018. Privacy and fairness in recommender systems via adversarial training of user representations. arXiv preprint arXiv:1807.03521 (2018).","journal-title":"arXiv preprint arXiv:1807.03521"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1145\/3460231.3473320","volume-title":"Fifteenth ACM Conference on Recommender Systems","author":"Saito Yuta","year":"2021","unstructured":"Yuta Saito and Thorsten Joachims. 2021. Counterfactual learning and evaluation for recommender systems: Foundations, implementations, and recent advances. In Fifteenth ACM Conference on Recommender Systems. 828\u2013830."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0553-6"},{"key":"e_1_3_2_45_2","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"12","author":"Sutton Richard S.","year":"1999","unstructured":"Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems 12 (1999).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_46_2","first-page":"814","volume-title":"International Conference on Machine Learning","author":"Swaminathan Adith","year":"2015","unstructured":"Adith Swaminathan and Thorsten Joachims. 2015. Counterfactual risk minimization: Learning from logged bandit feedback. In International Conference on Machine Learning. PMLR, 814\u2013823."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463005"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1198\/10618600152418584"},{"key":"e_1_3_2_49_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_50_2","article-title":"Counterfactual explanations for machine learning: A review","author":"Verma Sahil","year":"2020","unstructured":"Sahil Verma, John Dickerson, and Keegan Hines. 2020. Counterfactual explanations for machine learning: A review. arXiv preprint arXiv:2010.10596 (2020).","journal-title":"arXiv preprint arXiv:2010.10596"},{"key":"e_1_3_2_51_2","article-title":"Counterfactual explanations for machine learning: Challenges revisited","author":"Verma Sahil","year":"2021","unstructured":"Sahil Verma, John Dickerson, and Keegan Hines. 2021. Counterfactual explanations for machine learning: Challenges revisited. arXiv preprint arXiv:2106.07756 (2021).","journal-title":"arXiv preprint arXiv:2106.07756"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_55_2","article-title":"Causal disentanglement for semantics-aware intent learning in recommendation","author":"Wang Xiangmeng","year":"2022","unstructured":"Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, and Guandong Xu. 2022. Causal disentanglement for semantics-aware intent learning in recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_56_2","article-title":"Causal neural graph collaborative filtering","author":"Wang Xiangmeng","year":"2023","unstructured":"Xiangmeng Wang, Qian Li, Dianer Yu, Wei Huang, and Guandong Xu. 2023. Causal neural graph collaborative filtering. arXiv preprint arXiv:2307.04384 (2023).","journal-title":"arXiv preprint arXiv:2307.04384"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3629172"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3354077"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532021"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","unstructured":"Xiangmeng Wang Qian Li Dianer Yu and Guandong Xu. 2022. Off-policy learning over heterogeneous information for recommendation(WWW\u201922). Association for Computing Machinery New York NY USA 2348\u20132359. DOI:10.1145\/3485447.3512072","DOI":"10.1145\/3485447.3512072"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/978-3-030-47426-3_14","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Wang Xiangmeng","year":"2020","unstructured":"Xiangmeng Wang, Qian Li, Wu Zhang, Guandong Xu, Shaowu Liu, and Wenhao Zhu. 2020. Joint relational dependency learning for sequential recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 168\u2013180."},{"key":"e_1_3_2_62_2","article-title":"A survey on the fairness of recommender systems","author":"Wang Yifan","year":"2022","unstructured":"Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2022. A survey on the fairness of recommender systems. ACM Journal of the ACM (JACM) (2022).","journal-title":"ACM Journal of the ACM (JACM)"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462855"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"issue":"5","key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1111\/resp.12312","article-title":"Introduction to propensity scores","volume":"19","author":"Williamson Elizabeth J.","year":"2014","unstructured":"Elizabeth J. Williamson and Andrew Forbes. 2014. Introduction to propensity scores. Respirology 19, 5 (2014), 625\u2013635.","journal-title":"Respirology"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1093\/0195155270.001.0001"},{"issue":"5","key":"e_1_3_2_67_2","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4425\u20134445.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482244"},{"key":"e_1_3_2_69_2","first-page":"1","volume-title":"2020 IEEE Symposium on Computers and Communications (ISCC)","author":"Xu Jin","year":"2020","unstructured":"Jin Xu, Zishan Li, Bowen Du, Miaomiao Zhang, and Jing Liu. 2020. Reluplex made more practical: Leaky ReLU. In 2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, 1\u20137."},{"key":"e_1_3_2_70_2","first-page":"5453","volume-title":"International Conference on Machine Learning","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In International Conference on Machine Learning. PMLR, 5453\u20135462."},{"key":"e_1_3_2_71_2","article-title":"Beyond parity: Fairness objectives for collaborative filtering","volume":"30","author":"Yao Sirui","year":"2017","unstructured":"Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3322403"},{"key":"e_1_3_2_73_2","first-page":"249","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Yu Dianer","year":"2022","unstructured":"Dianer Yu, Qian Li, Xiangmeng Wang, Zhichao Wang, Yanan Cao, and Guandong Xu. 2022. Semantics-guided disentangled learning for recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 249\u2013261."},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.01.089"},{"key":"e_1_3_2_75_2","first-page":"289","article-title":"A causal view on robustness of neural networks","volume":"33","author":"Zhang Cheng","year":"2020","unstructured":"Cheng Zhang, Kun Zhang, and Yingzhen Li. 2020. A causal view on robustness of neural networks. Advances in Neural Information Processing Systems 33 (2020), 289\u2013301.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s00371-019-01691-w","article-title":"Stylistic scene enhancement GAN: Mixed stylistic enhancement generation for 3D indoor scenes","volume":"35","author":"Zhang Suiyun","year":"2019","unstructured":"Suiyun Zhang, Zhizhong Han, Yu-Kun Lai, Matthias Zwicker, and Hui Zhang. 2019. Stylistic scene enhancement GAN: Mixed stylistic enhancement generation for 3D indoor scenes. The Visual Computer 35, 6 (2019), 1157\u20131169.","journal-title":"The Visual Computer"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462908"},{"key":"e_1_3_2_78_2","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume":"31","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 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643670","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3643670","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:33Z","timestamp":1750291533000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":77,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3643670"],"URL":"https:\/\/doi.org\/10.1145\/3643670","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,22]]},"assertion":[{"value":"2023-06-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}