mlsanityData to 7 Oct 2026

Bayesian Structure Learning with Generative Flow Networks

arXiv 2202.13903 ↗First posted 28 Feb 2022cs.LG stat.MLcited by 153

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Abstract (arXiv)

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling of discrete and composite objects, such as graphs. In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. Generating a sample DAG from this approximate distribution is viewed as a sequential decision problem, where the graph is constructed one edge at a time, based on learned transition probabilities. Through evaluation on both simulated and real data, we show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs, and it compares favorably against other methods based on MCMC or variational inference.

Key facts. Cited by 153 papers in the map (data through 7 Oct 2026). Builds on 19 of its 60 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

19 of 60 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  3. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  4. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  5. 2021GFlowNet Foundationscited by 286
  6. 2018Learning Deep Generative Models of Graphscited by 227
  7. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  8. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  9. 2018Deep Reinforcement Learning and the Deadly Triadcited by 170
  10. 2019Causal Discovery with Reinforcement Learningcited by 145
  11. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  12. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
7 more
  1. 2013Learning Gaussian Networkscited by 62
  2. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  3. 2015Partition MCMC for inference on acyclic digraphscited by 33
  4. 2020Towards Scalable Bayesian Learning of Causal DAGscited by 33
  5. 2014Addendum on the scoring of Gaussian directed acyclic graphical modelscited by 32
  6. 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 31
  7. 2018Efficient Sampling and Structure Learning of Bayesian Networkscited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 17; 2023: 44; 2024: 40; 2025: 25; 2026: 27.

153 papers.

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  1. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 129
  2. 2022GFlowNets and variational inferencecited by 103
  3. 2023A theory of continuous generative flow networkscited by 98
  4. 2023Amortizing intractable inference in large language modelscited by 94
  5. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 83
  6. 2023Towards Understanding and Improving GFlowNet Trainingcited by 74
  7. 2023Robust Scheduling with GFlowNetscited by 67
  8. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  9. 2023Generative Flow Networks as Entropy-Regularized RLcited by 60
  10. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  11. 2024Learning diverse attacks on large language models for robust red-teaming and safety tuningcited by 54
  12. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
38 more
  1. 2022Generative Augmented Flow Networkscited by 48
  2. 2023Sample-efficient Multi-objective Molecular Optimization with GFlowNetscited by 48
  3. 2024Improved off-policy training of diffusion samplerscited by 47
  4. 2023Local Search GFlowNetscited by 45
  5. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  6. 2023Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetscited by 40
  7. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 40
  8. 2022A Variational Perspective on Generative Flow Networkscited by 39
  9. 2022Active Bayesian Causal Inferencecited by 34
  10. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  11. 2023Stochastic Generative Flow Networkscited by 32
  12. 2023Thompson sampling for improved exploration in GFlowNetscited by 31
  13. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  14. 2023Maximum entropy GFlowNets with soft Q-learningcited by 30
  15. 2024Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNetscited by 30
  16. 2023Distributional GFlowNets with Quantile Flowscited by 28
  17. 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetscited by 27
  18. 2023DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networkscited by 27
  19. 2024Improving GFlowNets for Text-to-Image Diffusion Alignmentcited by 27
  20. 2023Learning to Scale Logits for Temperature-Conditional GFlowNetscited by 26
  21. 2022Unifying Generative Models with GFlowNets and Beyondcited by 24
  22. 2024Adaptive teachers for amortized samplerscited by 23
  23. 2022GFlowOut: Dropout with Generative Flow Networkscited by 22
  24. 2022Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communicationscited by 20
  25. 2023PhyloGFN: Phylogenetic inference with generative flow networkscited by 20
  26. 2023CFlowNets: Continuous Control with Generative Flow Networkscited by 19
  27. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  28. 2023Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networkscited by 16
  29. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 16
  30. 2025Learning to Sample Effective and Diverse Prompts for Text-to-Image Generationcited by 16
  31. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  32. 2025Offline Model-Based Optimization: Comprehensive Reviewcited by 14
  33. 2023Computably Continuous Reinforcement-Learning Objectives are PAC-learnablecited by 13
  34. 2023Order-Preserving GFlowNetscited by 13
  35. 2024Pessimistic Backward Policy for GFlowNetscited by 13
  36. 2023Generative Flow Networks: a Markov Chain Perspectivecited by 12
  37. 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
  38. 2024Can a Bayesian Oracle Prevent Harm from an Agent?cited by 12

50 most cited of 153 All 153 (JSON)