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Structural transfer learning of non-Gaussian DAG

arXiv 2310.10239 ↗First posted 16 Oct 2023stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes. Yet, the available data in one single study is often limited for accurate DAG reconstruction, whereas heterogeneous data may be collected from multiple relevant studies. It remains an open question how to pool the heterogeneous data together for better DAG structure reconstruction in the target study. In this paper, we first introduce a novel set of structural similarity measures for DAG and then present a transfer DAG learning framework by effectively leveraging information from auxiliary DAGs of different levels of similarities. Our theoretical analysis shows substantial improvement in terms of DAG reconstruction in the target study, even when no auxiliary DAG is overall similar to the target DAG, which is in sharp contrast to most existing transfer learning methods. The advantage of the proposed transfer DAG learning is also supported by extensive numerical experiments on both synthetic data and multi-site brain functional connectivity network data.

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References · 14

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,157
  2. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 90
  3. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 70
  4. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 65
  5. 2018On Causal Discovery with Equal Variance Assumptioncited by 58
  6. 2017Learning linear structural equation models in polynomial time and sample complexitycited by 50
  7. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  8. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38
  9. 2018High-Dimensional Causal Discovery Under non-Gaussianitycited by 24
  10. 2021Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approachcited by 20
  11. 2018High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Modelscited by 12
  12. 2021Multi-task Learning of Order-Consistent Causal Graphscited by 10
2 more
  1. 2021Learning linear non-Gaussian directed acyclic graph with diverging number of nodescited by 6
  2. 2018Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variancescited by 2

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