Learning to Discover Sparse Graphical Models

Abstract : We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. Popular methods rely on estimating a penalized maximum likelihood of the precision matrix. However, in these approaches structure recovery is an indirect consequence of the data-fit term, the penalty can be difficult to adapt for domain-specific knowledge, and the inference is computationally demanding. By contrast, it may be easier to generate training samples of data that arise from graphs with the desired structure properties. We propose here to leverage this latter source of information as training data to learn a function, parametrized by a neural network that maps empirical covariance matrices to estimated graph structures. Learning this function brings two benefits: it implicitly models the desired structure or sparsity properties to form suitable priors, and it can be tailored to the specific problem of edge structure discovery, rather than maximizing data likelihood. Applying this framework, we find our learnable graph-discovery method trained on synthetic data generalizes well: identifying relevant edges in both synthetic and real data, completely unknown at training time. We find that on genetics, brain imaging, and simulation data we obtain performance generally superior to analytical methods.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.inria.fr/hal-01306491
Contributor : Eugene Belilovsky <>
Submitted on : Wednesday, August 2, 2017 - 7:47:00 PM
Last modification on : Wednesday, April 17, 2019 - 12:15:35 PM

Files

DataDrivenGraphDiscovery_NIPS....
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01306491, version 4
  • ARXIV : 1605.06359

Citation

Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew Blaschko. Learning to Discover Sparse Graphical Models. International Conference on Machine Learning , Aug 2017, Sydney, Australia. ⟨hal-01306491v4⟩

Share

Metrics

Record views

530

Files downloads

269