mlsanityData to 14 Oct 2026

Learning Discrete Directed Acyclic Graphs via Backpropagation

arXiv 2210.15353 ↗First posted 27 Oct 2022cs.LG cs.AI stat.MLcited by 3

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Abstract (arXiv)

Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. However, a number of techniques for fully discrete backpropagation could instead be applied. In this paper, we explore that direction and propose DAG-DB, a framework for learning DAGs by Discrete Backpropagation. Based on the architecture of Implicit Maximum Likelihood Estimation [I-MLE, arXiv:2106.01798], DAG-DB adopts a probabilistic approach to the problem, sampling binary adjacency matrices from an implicit probability distribution. DAG-DB learns a parameter for the distribution from the loss incurred by each sample, performing competitively using either of two fully discrete backpropagation techniques, namely I-MLE and Straight-Through Estimation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

22 of 47 references.

References · 22

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10 more
  1. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.

  1. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  2. 2025Effects of Distributional Biases on Gradient-Based Causal Discovery in the Bivariate Categorical Casecited by 0
  3. 2026GraIP: A Benchmarking Framework For Neural Graph Inverse Problemscited by 0