[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125525-en":3,"doc-seo-125525-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125525,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","A Symmetric Loss Perspective of Reliable Machine Learning","Binary classification often replaces zero-one loss with optimizable surrogate losses such as logistic, hinge, or sigmoid, yet the choice can strongly affect classifier performance. Symmetric (symmetric-condition) losses have proven useful for learning under corrupted labels. This overview reviews how symmetric losses enable robust classification via BER minimization and AUC maximization, then shows benefits for natural language processing from relevant keywords and unlabeled documents, concluding with future directions for reliable machine learning.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \n[provided by](provided by arXiv.org)[ arXiv.org](provided by arXiv.org) e-Print Archive  \narXiv :2101 .01366v1 [ stat .ML] 5 Jan 2021  \nA Symmetric Loss Perspective of Reliable Machine Learning  \nNontawat Charoenphakdee 1 ;2 Jongyeong Lee 1 ;2 Masashi Sugiyama2 ; 1  \n1 The University of Tokyo 2 RIKEN AIP  \nAbstract  \nWhen minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to optimize. Examples of well-known surrogate losses for binary classification include the logistic loss, hinge loss, and sigmoid loss. It is known that the choice of a surrogate loss can highly influence the performance of the trained classifierand therefore it should be carefully chosen. Recently, surrogate losses that satisfy a certain symmetric condition (aka., symmetric losses) have demonstrated their usefulness in learning from corrupted labels. In this article, we provide an overview of symmetric losses and their applications. First, we review how a symmetric loss can yield robust classification from corrupted labels in balanced error rate (BER) minimization and area under the receiver operating characteristic curve (AUC) maximization. Then, we demonstrate how the robust AUC maximization method can benefit natural language processing in the problem where we want to learn only from relevant keywords and unlabeled documents. Finally, we conclude this article by discussing future directions, including potential applications of symmetric losses for reliable machine learning and the design of non-symmetric losses that can benefit from the symmetric condition.  \n1 Introduction  \nModern machine learning methods such as deep learning typically require a large amount of data to achieve desirable performance [Schmidhuber, 2015 ; LeCun et al., 2015 ; Goodfellow et al., 2016] . However, it is often the case that the labeling process is costly and time-consuming. To mitigate this problem, one may consider collecting training labels through crowdsourcing [Dawid and Skene, 1979 ; Kittur et al., 2008], which is a popular approach and has become more convenient in the recent years [Deng et al., 2009 ; Crowston, 2012 ; Sun et al., 2014 ; Vaughan, 2017 ; Pandey et al., 2020 ; Vermicelli et al., 2020 ; Washington et al., 2020] . For example, crowdsourcing has been used for tackling the COVID-19 pandemic to accelerate research and drug discovery [Vermicelli et al., 2020 ; Chodera et al., 2020] . However, a big challenge of crowdsourcing is that the collected labels can be unreliable because of non-expert annotators fail to provide correct information [Lease, 2011 ; Zhang et al., 2014 ; Gao et al., 2016 ; Imamura et al., 2018] . Not only the non-expert error, but even expert annotators can also make mistakes. As a result, it is unrealistic to always expect that the collected training data are always reliable.  \nIt is well-known that training from data with noisy labels can give an inaccurate classifier [Aslam and Decatur, 1996 ; Biggio et al., 2011 ; Cesa-Bianchi et al., 1999 ; Frnay and Verleysen, 2013 ; Natarajan et al., 2013] . Interestingly, it has been shown that the trained classifier may only perform slightly better than random guessing even under a simple noise assumption [Long and Servedio, 2010] . Since learning from noisy labels is challenging and highly relevant in the real-world, this problem has been studied extensively in both theoretical and practical aspects [Van Rooyen and Williamson, 2017 ; Jiang et al., 2018 ; Algan and Ulusoy, 2019 ; Liu and Guo, 2020 ; Wei and Liu, 2020 ; Karimi et al., 2020 ; Han et al., 2018, 2020] .  \nRecently, a loss function that satisfies a certain symmetric condition has demonstrated its usefulness in learning from noisy labels. A pioneer work in this direction is the work by Manwani and Sastr","cbCaific2b0petxa","https://ap.wps.com/l/cbCaific2b0petxa","pdf",550915,1,23,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"Why are surrogate losses used in binary classification instead of zero-one loss?\",\"answer\":\"Surrogate losses make the learning objective feasible to optimize. However, different surrogates can significantly influence the trained classifier’s performance.\"},{\"question\":\"How do symmetric losses help when labels are corrupted?\",\"answer\":\"Symmetric losses satisfy a symmetric condition that can yield robust classification under corrupted-label settings, including mutually contaminated noise models.\"},{\"question\":\"What natural language processing task is addressed using symmetric losses?\",\"answer\":\"Learning a reliable classifier using only relevant keywords and unlabeled documents, leveraging robust AUC maximization benefits in that weak supervision context.\"}]","A Symmetric Loss Perspective of Reliable Machine Learning | PDF",1785899670,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-symmetric-loss-perspective-of-reliable-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-symmetric-loss-perspective-of-reliable-machine-learning/125525/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are surrogate losses used in binary classification instead of zero-one loss?","Question",{"text":75,"@type":76},"Surrogate losses make the learning objective feasible to optimize. However, different surrogates can significantly influence the trained classifier’s performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do symmetric losses help when labels are corrupted?",{"text":80,"@type":76},"Symmetric losses satisfy a symmetric condition that can yield robust classification under corrupted-label settings, including mutually contaminated noise models.",{"name":82,"@type":73,"acceptedAnswer":83},"What natural language processing task is addressed using symmetric losses?",{"text":84,"@type":76},"Learning a reliable classifier using only relevant keywords and unlabeled documents, leveraging robust AUC maximization benefits in that weak supervision context.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]