{"type":"ScholarlyArticle","title":"Methods for recovering conditional independence graphs (Abstract Reprint)","authors":[{"name":"Harsh Shrivastava","slug":"harsh-shrivastava","given":"Harsh","family":"Shrivastava","affiliation":"Microsoft Research , Redmond , USA"},{"name":"Urszula Chajewska","slug":"urszula-chajewska","given":"Urszula","family":"Chajewska","affiliation":"Microsoft Research , Redmond , USA"}],"abstract":"Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations . To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes.\r\n\r\nKeywords: Conditional Independence Graphs, Probabilistic Graphical Models, Graphical Lasso, Deep Learning, Optimization","doi":"10.24963/ijcai.2024/956","url":"https://ijcai.org/proceedings/2024/956","pdf":"https://ijcai.org/proceedings/2024/0956.pdf","markdown":"https://ijcai.org/proceedings/2024/956.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/956","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Journal Track","track":"Journal Track","number":956,"pages":{"first":8484,"last":8484},"keywords":["Journal Track: Journal Track"],"videos":[],"references":[{"position":1,"raw":"[Shrivastava and Chajewska, 2024] Harsh Shrivastava and Urszula Chajewska. Methods for recovering conditional independence graphs: A survey. J. Artif. Int. Res., 80, jun 2024.","title":"Harsh Shrivastava and Urszula Chajewska. Methods for recovering conditional independence graphs: A survey","authors":"Chajewska Shrivastava","year":"2024","venue":"J. Artif. Int. Res","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}