mlsanityData to 14 Oct 2026

Methods for Recovering Conditional Independence Graphs: A Survey

arXiv 2211.06829 ↗First posted 13 Nov 2022cs.LG stat.MLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 30 of its 84 references that are in the map. First posted on arXiv 13 Nov 2022.

Builds on

30 of 84 references.

References · 30

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18 more
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Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 2.

7 papers.

  1. 2023Neural Graph Revealerscited by 3
  2. 2023Federated Learning with Neural Graphical Modelscited by 2
  3. 2023Learning Multi-Frequency Partial Correlation Graphscited by 1
  4. 2023Are uGLAD? Time will tell!cited by 0
  5. 2023Knowledge Propagation over Conditional Independence Graphscited by 0
  6. 2024Generative Kaleidoscopic Networkscited by 0
  7. 2024Obstacle-aware Gaussian Process Regressioncited by 0