mlsanityData to 14 Oct 2026

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees

arXiv 2504.16356 ↗First posted 23 Apr 2025stat.ML cs.LG stat.MEcited by 0

Not on the map. Why?

Abstract (arXiv)

Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to estimate it. The method allows for flexible functional dependency on the covariate, and fits the data reasonably well in the absence of a Gaussianity assumption. Theoretical results with PAC guarantees are established for the method, under assumptions commonly used in an Empirical Risk Minimization framework. The performance of the proposed method is evaluated on several synthetic data settings and benchmarked against existing approaches. The method is further illustrated on real datasets involving data from neuroscience and finance, respectively, and produces interpretable results.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 60 references that are in the map. First posted on arXiv 23 Apr 2025.

Builds on

17 of 60 references.

References · 17

  1. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  2. 2017Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networkscited by 311
  3. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  4. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 87
  5. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 45
  6. 2021Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n)cited by 42
  7. 2020On the representation and learning of monotone triangular transport mapscited by 41
  8. 2013On Graphical Models via Univariate Exponential Family Distributionscited by 26
  9. 2017Contextual Explanation Networkscited by 21
  10. 2021Learning non-Gaussian graphical models via Hessian scores and triangular transportcited by 16
  11. 2022Causal Discovery with Heterogeneous Observational Datacited by 13
  12. 2020The huge Package for High-dimensional Undirected Graph Estimation in Rcited by 9
5 more
  1. 2023Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networkscited by 7
  2. 2020High Dimensional Gaussian Graphical Regression Models with Covariatescited by 3
  3. 2023Contextual Directed Acyclic Graphscited by 3
  4. 2024Bayesian Covariate-Dependent Graph Learning with a Dual Group Spike-and-Slab Priorcited by 3
  5. 2023Covariate-Assisted Bayesian Graph Learning for Heterogeneous Datacited by 2

Built on by

Not cited yet.