{"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/fast-and-scalable-learning-of-sparse-changes","title":"Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure","arxiv_id":"1710.11223","date":"2017-10-30","proceeding":null,"authors":["Beilun Wang","Arshdeep Sekhon","Yanjun Qi"],"abstract":"We focus on the problem of estimating the change in the dependency structures\nof two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for\nsparse change estimation in GGMs involve expensive and difficult non-smooth\noptimization. We propose a novel method, DIFFEE for estimating DIFFerential\nnetworks via an Elementary Estimator under a high-dimensional situation. DIFFEE\nis solved through a faster and closed form solution that enables it to work in\nlarge-scale settings. We conduct a rigorous statistical analysis showing that\nsurprisingly DIFFEE achieves the same asymptotic convergence rates as the\nstate-of-the-art estimators that are much more difficult to compute. Our\nexperimental results on multiple synthetic datasets and one real-world data\nabout brain connectivity show strong performance improvements over baselines,\nas well as significant computational benefits.","url_abs":"http://arxiv.org/abs/1710.11223v3","url_pdf":"http://arxiv.org/pdf/1710.11223v3.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":"fast-and-scalable-learning-of-sparse-changes","repo_url":"https://github.com/QData/DIFFEE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"fast-and-scalable-learning-of-sparse-changes","repo_url":"https://github.com/QData/JointNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}