{"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/mida-multiple-imputation-using-denoising","title":"MIDA: Multiple Imputation using Denoising Autoencoders","arxiv_id":"1705.02737","date":"2017-05-08","proceeding":null,"authors":["Lovedeep Gondara","Ke Wang"],"abstract":"Missing data is a significant problem impacting all domains. State-of-the-art\nframework for minimizing missing data bias is multiple imputation, for which\nthe choice of an imputation model remains nontrivial. We propose a multiple\nimputation model based on overcomplete deep denoising autoencoders. Our\nproposed model is capable of handling different data types, missingness\npatterns, missingness proportions and distributions. Evaluation on several real\nlife datasets show our proposed model significantly outperforms current\nstate-of-the-art methods under varying conditions while simultaneously\nimproving end of the line analytics.","url_abs":"http://arxiv.org/abs/1705.02737v3","url_pdf":"http://arxiv.org/pdf/1705.02737v3.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":"mida-multiple-imputation-using-denoising","repo_url":"https://github.com/HarryK24/MIDA-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mida-multiple-imputation-using-denoising","repo_url":"https://github.com/ambareeshsrja16/Python-Module-for-Missing-Data-Imputation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mida-multiple-imputation-using-denoising","repo_url":"https://github.com/harry24k/mida-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"imputation","task_name":"Imputation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02737","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}