# Learning of Discrete Graphical Models with Neural Networks

@article{Jayakumar2020LearningOD, title={Learning of Discrete Graphical Models with Neural Networks}, author={Abhijith Jayakumar and Andrey Y. Lokhov and Sidhant Misra and Marc Vuffray}, journal={ArXiv}, year={2020}, volume={abs/2006.11937} }

Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the…

## 3 Citations

### Learning Continuous Exponential Families Beyond Gaussian

- Computer ScienceArXiv
- 2021

This work introduces a computationally efficient method for learning continuous graphical models based on the Interaction Screening approach that maintains similar requirements in terms of accuracy and sample complexity compared to alternative approaches such as maximization of conditional likelihood, while considerably improving upon the algorithm’s run-time.

### Reconstruction of pairwise interactions using energy-based models

- Computer ScienceMSML
- 2021

It is shown that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions, and this work proposes an approach based on energy-based models and pseudolikelihood maximization to address these complications.

### Efficient learning of discrete graphical models

- Computer ScienceNeurIPS
- 2020

This work provides the first sample-efficient method based on the interaction screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis.

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