---
title: "Methods for recovering conditional independence graphs (Abstract Reprint)"
authors:
  - "Harsh Shrivastava"
  - "Urszula Chajewska"
doi: "10.24963/ijcai.2024/956"
url: "https://ijcai.org/proceedings/2024/956"
pdf: "https://ijcai.org/proceedings/2024/0956.pdf"
venue: "Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence"
year: 2024
pages: "8484-8484"
track: "Journal Track"
keywords:
  - "Journal Track: Journal Track"
---

# Methods for recovering conditional independence graphs (Abstract Reprint)

Harsh Shrivastava, Urszula Chajewska

*Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, pages 8484-8484. DOI: [10.24963/ijcai.2024/956](https://doi.org/10.24963/ijcai.2024/956)

## 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.

Keywords: Conditional Independence Graphs, Probabilistic Graphical Models, Graphical Lasso, Deep Learning, Optimization

## Full text

## Methods for recovering conditional independence graphs (Abstract Reprint)

Harsh Shrivastava and Urszula Chajewska

Microsoft Research, Redmond, USA

hshrivastava@microsoft.com, urszc@microsoft.com

Abstract Reprint. This is an abstract reprint of a journal by [Shrivastava and Chajewska, 2024].

## 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 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.

## References

1. [Shrivastava and Chajewska, 2024] Harsh Shrivastava and Urszula Chajewska. Methods for recovering conditional independence graphs: A survey. J. Artif. Int. Res., 80, jun 2024.