{"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/dealing-with-missing-data-using-attention-and","title":"Dealing with missing data using attention and latent space regularization","arxiv_id":"2211.07059","date":"2022-11-14","proceeding":null,"authors":["Jahan C. Penny-Dimri","Christoph Bergmeir","Julian Smith"],"abstract":"Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating process. Here we develop a theoretical framework for training and inference using only observed variables enabling modeling of incomplete datasets without imputation. Using an information and measure-theoretic argument we construct models with latent space representations that regularize against the potential bias introduced by missing data. The theoretical properties of this approach are demonstrated empirically using a synthetic dataset. The performance of this approach is tested on 11 benchmarking datasets with missingness and 18 datasets corrupted across three missingness patterns with comparison against a state-of-the-art model and industry-standard imputation. We show that our proposed method overcomes the weaknesses of imputation methods and outperforms the current state-of-the-art.","url_abs":"https://arxiv.org/abs/2211.07059v1","url_pdf":"https://arxiv.org/pdf/2211.07059v1.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":"dealing-with-missing-data-using-attention-and","repo_url":"https://github.com/jahanpd/missingness","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"imputation","task_name":"Imputation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}