Methods for recovering conditional independence graphs (Abstract Reprint)
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Journal Track. Pages 8484-8484.
https://doi.org/10.24963/ijcai.2024/956
Keywords
- Journal Track: Journal Track
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
- [Shrivastava and Chajewska, 2024] Harsh Shrivastava and Urszula Chajewska. Methods for recovering conditional independence graphs: A survey. J. Artif. Int. Res., 80, jun 2024.
References were extracted automatically from the PDF and may contain errors.
Full text extracted automatically from the PDF; figures, tables and equations may differ. The PDF is the version of record.
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.
@inproceedings{ijcai2024p956,
title = {Methods for recovering conditional independence graphs (Abstract Reprint)},
author = {Shrivastava, Harsh and Chajewska, Urszula},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8484--8484},
year = {2024},
month = {8},
note = {Journal Track},
doi = {10.24963/ijcai.2024/956},
url = {https://doi.org/10.24963/ijcai.2024/956},
}
TY - CPAPER TI - Methods for recovering conditional independence graphs (Abstract Reprint) AU - Shrivastava, Harsh AU - Chajewska, Urszula T2 - Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2024 SP - 8484 EP - 8484 DO - 10.24963/ijcai.2024/956 UR - https://doi.org/10.24963/ijcai.2024/956 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
Welcome
Future Conferences
AI Journal Division
IJCAI Proceedings
About IJCAI
IJCAI Awards