{"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/a-fast-and-scalable-joint-estimator-for","title":"A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models","arxiv_id":"1702.02715","date":"2017-02-09","proceeding":null,"authors":["Beilun Wang","Ji Gao","Yanjun Qi"],"abstract":"Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many\nrelated tasks (large $K$) under a high-dimensional (large $p$) situation is an\nimportant task. Most previous studies for the joint estimation of multiple\nsGGMs rely on penalized log-likelihood estimators that involve expensive and\ndifficult non-smooth optimizations. We propose a novel approach, FASJEM for\n\\underline{fa}st and \\underline{s}calable \\underline{j}oint\nstructure-\\underline{e}stimation of \\underline{m}ultiple sGGMs at a large\nscale. As the first study of joint sGGM using the Elementary Estimator\nframework, our work has three major contributions: (1) We solve FASJEM through\nan entry-wise manner which is parallelizable. (2) We choose a proximal\nalgorithm to optimize FASJEM. This improves the computational efficiency from\n$O(Kp^3)$ to $O(Kp^2)$ and reduces the memory requirement from $O(Kp^2)$ to\n$O(K)$. (3) We theoretically prove that FASJEM achieves a consistent estimation\nwith a convergence rate of $O(\\log(Kp)/n_{tot})$. On several synthetic and four\nreal-world datasets, FASJEM shows significant improvements over baselines on\naccuracy, computational complexity, and memory costs.","url_abs":"http://arxiv.org/abs/1702.02715v3","url_pdf":"http://arxiv.org/pdf/1702.02715v3.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":"a-fast-and-scalable-joint-estimator-for","repo_url":"https://github.com/QData/JointNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-fast-and-scalable-joint-estimator-for","repo_url":"https://github.com/qdata/fasjem","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}