{"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/structure-learning-of-sparse-ggms-over","title":"Structure Learning of Sparse GGMs over Multiple Access Networks","arxiv_id":"1812.10437","date":"2018-12-26","proceeding":null,"authors":["Mostafa Tavassolipour","Armin Karamzade","Reza Mirzaeifard","Seyed Abolfazl Motahari","Mohammad-Taghi Manzuri Shalmani"],"abstract":"A central machine is interested in estimating the underlying structure of a\nsparse Gaussian Graphical Model (GGM) from datasets distributed across multiple\nlocal machines. The local machines can communicate with the central machine\nthrough a wireless multiple access channel. In this paper, we are interested in\ndesigning effective strategies where reliable learning is feasible under power\nand bandwidth limitations. Two approaches are proposed: Signs and Uncoded\nmethods. In Signs method, the local machines quantize their data into binary\nvectors and an optimal channel coding scheme is used to reliably send the\nvectors to the central machine where the structure is learned from the received\ndata. In Uncoded method, data symbols are scaled and transmitted through the\nchannel. The central machine uses the received noisy symbols to recover the\nstructure. Theoretical results show that both methods can recover the structure\nwith high probability for large enough sample size. Experimental results\nindicate the superiority of Signs method over Uncoded method under several\ncircumstances.","url_abs":"http://arxiv.org/abs/1812.10437v1","url_pdf":"http://arxiv.org/pdf/1812.10437v1.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":"structure-learning-of-sparse-ggms-over","repo_url":"https://github.com/ArminKaramzade/distributed-sparse-GGM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}