{"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/unlearn-what-you-have-learned-adaptive-crowd","title":"Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners","arxiv_id":"1804.06481","date":"2018-04-17","proceeding":null,"authors":["Yao Zhou","Arun Reddy Nelakurthi","Jingrui He"],"abstract":"With the increasing demand for large amount of labeled data, crowdsourcing\nhas been used in many large-scale data mining applications. However, most\nexisting works in crowdsourcing mainly focus on label inference and incentive\ndesign. In this paper, we address a different problem of adaptive crowd\nteaching, which is a sub-area of machine teaching in the context of\ncrowdsourcing. Compared with machines, human beings are extremely good at\nlearning a specific target concept (e.g., classifying the images into given\ncategories) and they can also easily transfer the learned concepts into similar\nlearning tasks. Therefore, a more effective way of utilizing crowdsourcing is\nby supervising the crowd to label in the form of teaching. In order to perform\nthe teaching and expertise estimation simultaneously, we propose an adaptive\nteaching framework named JEDI to construct the personalized optimal teaching\nset for the crowdsourcing workers. In JEDI teaching, the teacher assumes that\neach learner has an exponentially decayed memory. Furthermore, it ensures\ncomprehensiveness in the learning process by carefully balancing teaching\ndiversity and learner's accurate learning in terms of teaching usefulness.\nFinally, we validate the effectiveness and efficacy of JEDI teaching in\ncomparison with the state-of-the-art techniques on multiple data sets with both\nsynthetic learners and real crowdsourcing workers.","url_abs":"http://arxiv.org/abs/1804.06481v2","url_pdf":"http://arxiv.org/pdf/1804.06481v2.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":"unlearn-what-you-have-learned-adaptive-crowd","repo_url":"https://github.com/collwe/JEDI-Crowd-Teaching","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06481","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}