{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-sampling-for-coarse-ranking","title":"Adaptive Sampling for Coarse Ranking","arxiv_id":"1802.07176","date":"2018-02-20","proceeding":null,"authors":["Sumeet Katariya","Lalit Jain","Nandana Sengupta","James Evans","Robert Nowak"],"abstract":"We consider the problem of active coarse ranking, where the goal is to sort\nitems according to their means into clusters of pre-specified sizes, by\nadaptively sampling from their reward distributions. This setting is useful in\nmany social science applications involving human raters and the approximate\nrank of every item is desired. Approximate or coarse ranking can significantly\nreduce the number of ratings required in comparison to the number needed to\nfind an exact ranking. We propose a computationally efficient PAC algorithm\nLUCBRank for coarse ranking, and derive an upper bound on its sample\ncomplexity. We also derive a nearly matching distribution-dependent lower\nbound. Experiments on synthetic as well as real-world data show that LUCBRank\nperforms better than state-of-the-art baseline methods, even when these methods\nhave the advantage of knowing the underlying parametric model.","url_abs":"http://arxiv.org/abs/1802.07176v1","url_pdf":"http://arxiv.org/pdf/1802.07176v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-sampling-for-coarse-ranking","repo_url":"https://github.com/sumeetsk/coarse_ranking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07176","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}