[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122096-en":3,"doc-seo-122096-105":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":4,"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},122096,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Fast and Robust Rank Aggregation against Model Misspecification","Rank aggregation (RA) summarizes multiple users’ preferences into a single total order under a homogeneity assumption. In real-world settings, model misspecification emerges when this assumption fails. Many robust RA methods depend on specific noise-perturbation assumptions, limiting real-world generalization. This work introduces CoarsenRank, robust to mild misspecification by performing rank aggregation over a neighborhood defined through empirical data distributions, with an exponential prior and simplified posterior under divergence measures.","Fast and Robust Rank Aggregation against Model Misspecification  \nYuangang Pan∗  \nCenter for Frontier AIResearch  \nResearch Agency for Science, Technology and Research (A*STAR) Singapore  \nand  \nAustralian Artificial Intelligence Institute University of Technology Sydney  \nNSW 2007, Australia Ivor W. Tsang†  \nCenter for Frontier AIResearch  \nResearch Agency for Science, Technology and Research (A*STAR) Singapore  \nand  \nAustralian Artificial Intelligence Institute University of Technology Sydney  \nNSW 2007, Australia  \nWeijie Chen  \nZhijiang College  \nZhejiang University of Technology Hangzhou 310014, Zhejiang, China  \nGang Niu  \nCenter for Advanced Intelligence Project RIKEN, Tokyo, 103-0027, Japan  \nMasashi Sugiyama  \nCenter for Advanced Intelligence Project RIKEN, Tokyo, 103-0027, Japan  \nand  \nGraduate School of Frontier Sciences University of Tokyo  \nChiba 277-8561, Japan  \n[Yuangang.Pan@gmail.com](Yuangang.Pan@gmail.com)  \n[Ivor.Tsang@gmail.com](Ivor.Tsang@gmail.com)  \n[wjcper2008@126.com](wjcper2008@126.com)  \n[gang.niu@riken.jp](gang.niu@riken.jp)  \n[sugi@k.u-tokyo.ac.jp](sugi@k.u-tokyo.ac.jp)  \nEditor: Sathiya Keerthi  \nAbstract  \nIn rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in RA arises since the homogeneity assumption fails to be satisfied in the complex real-world situation. Existing robust RAs usually resort to an augmentation of the ranking model to account for additional noises, where the collected preferences can be treated as a noisy perturbation of idealized preferences. Since the majority of robust RAs rely on certain perturbation assumptions, they cannot generalize well to  \n∗ . Preliminary work was done during an internship at RIKEN AIP.  \n†. The corresponding author.  \n©2022 Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu and Masashi Sugiyama.  \nLicense: CC-BY 4.0, see [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/. Attribution requirements)[. Attribution requirements](https://creativecommons.org/licenses/by/4.0/. Attribution requirements) are provided at  \n[http://jmlr.org/papers/v23/20-315.html](http://jmlr.org/papers/v23/20-315.html).  \nPan, Tsang, Chen, Niu and Sugiyama  \nagnostic noise-corrupted preferences in the real world. In this paper, we propose CoarsenRank, which possesses robustness against model misspecification. Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locate in a neighborhood of the actual preferences. (2) CoarsenRank then performs regular RAs over a neighborhood of the preferences instead of the original data set directly. Therefore, CoarsenRank enjoys robustness against model misspecification within a neighborhood. (3) The neighborhood of the data set is defined via their empirical data distributions. Further, we put an exponential prior on the unknown size of the neighborhood, and derive a much-simplified posterior formula for CoarsenRank under particular divergence measures. (4) CoarsenRank is further instantiated to Coarsened Thurstone, Coarsened Bradly-Terry, and Coarsened Plackett-Luce with three popular probability ranking models. Meanwhile, tractable optimization strategies are introduced with regards to each instantiation respectively. In the end, we apply CoarsenRank on four real-world data sets.  \nExperiments show that CoarsenRank is fast and robust, achieving consistent improvements over baseline methods.  \nKeywords: Robust Rank Aggregation, Model Misspecification, CoarsenRank, Coarsened BradlyTerry, Coarsened Plackett-Luce  \n1. Introduction  \nRank aggregation (RA) refers to the task of recovering the total order over a set of items, given a collection of pairwise/partial/full preferences over items (Lin, 2010) . Therefore, RA is a ","cbCaiqV5iGoAVWcC","https://ap.wps.com/l/cbCaiqV5iGoAVWcC","pdf",778698,1,35,"English","en",105,"# Introduction\n## Rank aggregation and its homogeneity assumption\n## Model misspecification and limitations of existing robust RA\n# CoarsenRank approach\n## Neighborhood definition via empirical distributions\n## Exponential prior and simplified posterior\n# Instantiations and optimization\n## Coarsened Thurstone\n## Coarsened Bradly-Terry\n## Coarsened Plackett-Luce\n# Experiments and results\n## Real-world data sets and performance comparisons","[{\"question\":\"What does model misspecification mean in rank aggregation (RA)?\",\"answer\":\"Model misspecification in RA refers to the inconsistency between collected ranking data and RA’s homogeneity assumption that users share the same ground-truth ordering and annotation accuracy.\"},{\"question\":\"How does CoarsenRank improve robustness under model misspecification?\",\"answer\":\"CoarsenRank assumes the ideal preferences exist near the actual preferences, defines a neighborhood using empirical data distributions, and then runs regular rank aggregation within that neighborhood to gain local robustness.\"},{\"question\":\"What models are used in CoarsenRank instantiations?\",\"answer\":\"CoarsenRank is instantiated into Coarsened Thurstone, Coarsened Bradly-Terry, and Coarsened Plackett-Luce, each paired with tractable optimization strategies.\"}]","Fast and Robust Rank Aggregation against Model Misspecification | 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does model misspecification mean in rank aggregation (RA)?","Question",{"text":75,"@type":76},"Model misspecification in RA refers to the inconsistency between collected ranking data and RA’s homogeneity assumption that users share the same ground-truth ordering and annotation accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CoarsenRank improve robustness under model misspecification?",{"text":80,"@type":76},"CoarsenRank assumes the ideal preferences exist near the actual preferences, defines a neighborhood using empirical data distributions, and then runs regular rank aggregation within that neighborhood to gain local robustness.",{"name":82,"@type":73,"acceptedAnswer":83},"What models are used in CoarsenRank instantiations?",{"text":84,"@type":76},"CoarsenRank is instantiated into Coarsened Thurstone, Coarsened Bradly-Terry, and Coarsened Plackett-Luce, each paired with tractable optimization 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