structure-learning

There are 58 repositories under structure-learning topic.

  • erdogant/bnlearn

    Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

    Language:Jupyter Notebook54079651

  • aimclub/BAMT

    Repository of a data modeling and analysis tool based on Bayesian networks

    Language:Python13385621

  • kevinsbello/dagma

    A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

    Language:Python1253522

  • py-why/dodiscover

    [Experimental] Global causal discovery algorithms

    Language:Python10677717

  • phlippe/ENCO

    Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

    Language:Python863515

  • probsys/AutoGP.jl

    Automated Bayesian model discovery for time series data

    Language:Julia805165

  • felixleopoldo/benchpress

    Scalable open-source software to run, develop, and benchmark causal discovery algorithms

    Language:Python7244717
  • GOLEM

    aimclub/GOLEM

    Graph Optimiser for Learning and Evolution of Models

    Language:Python70412411

  • larslorch/avici

    Amortized Inference for Causal Structure Learning, NeurIPS 2022

    Language:Python672410

  • larslorch/dibs

    DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

    Language:Python523214

  • hiarindam/document-image-classification-TL-SG

    Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

    Language:Python433615

  • agadetsky/pytorch-pl-variance-reduction

    [AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

    Language:Jupyter Notebook38232

  • salvaRC/Graphino

    Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".

    Language:Jupyter Notebook331213

  • arranger1044/spyn

    Sum-Product Network learning routines in python

    Language:Python27304
  • BNSL

    Howardhuang98/BNSL

    Bayesian network structure learning

    Language:Python18210

  • microsoft/ML4C

    [SDM'23] ML4C: Seeing Causality Through Latent Vicinity

    Language:Python12312

  • Duntrain/dagrad

    dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.

    Language:Python10301

  • ogencoglu/causal_twitter_modeling_covid19

    Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.

    Language:Jupyter Notebook10212

  • sfu-cl-lab/FactorBase

    The source code repository for the FactorBase system

    Language:Java102436

  • massimo-rizzoli/BNSL-QA-python

    Python implementation of Bayesian Network Structure Learning using Quantum Annealing https://doi.org/10.1140/epjst/e2015-02349-9

    Language:Python9111

  • QueensGambit/PGM-Causal-Reasoning

    Experiments on structure learning of Bayesian Networks with emphasis on finding causal relationship

    Language:Jupyter Notebook9502

  • Duntrain/TOPO

    Optimizing NOTEARS Objectives via Topological Swaps

    Language:Python8303

  • furrer-lab/abn

    Bayesian network analysis in R

    Language:R821220

  • syanga/dglearn

    Python implementation of "Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs," in ICML 2020

    Language:Python8210

  • sergioluengosanchez/TSEM

    Tractable learning of Bayesian networks from partially observed data

    Language:Python7000

  • syanga/model-augmented-mutual-information

    Code accompanying paper "Model-Augmented Conditional Mutual Information Estimation for Feature Selection" in UAI 2020

    Language:Jupyter Notebook6200

  • fritzbayer/Causal-Discovery-Research-Papers

    A curated list of causal structure learning research papers with implementations.

    5100

  • HeddaCohenIndelman/PerturbedStructuredPredictorsDirect

    This is the official implementation of the bipartite matching experiment from the paper "Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization".

    Language:Python5101

  • montilab/shine

    Structure Learning for Hierarchical Networks

    Language:R5302

  • miicTeam/miicsearchscore

    [ICML 2025] R implementation of MIIC_search&score: a search-and-score algorithm for learning ancestral graphs with latent confounders, using multivariate information over ac-connected subset.

    Language:R4

  • tmadeira/ktrees

    Computer Science undergraduate thesis: Uniform Generation of k-trees for Learning the Structure of Bayesian Networks (USP 2016).

    Language:Go4201

  • werkaaa/iscm

    Standardizing Structural Causal Models, ICLR 2025

    Language:Jupyter Notebook4

  • jspieler/QBAF-Learning

    Structure Learning of Gradual Bipolar Argumentation Graphs using Genetic Algorithms

    Language:Python3101

  • semoglou/ksil

    A silhouette-guided instance-weighted k-means algorithm that integrates silhouette scores into the clustering process to improve clustering quality.

    Language:Jupyter Notebook3

  • VishnuBeji/BayesianNet_QuantumAnnealing

    Bayesian Network structure learning with encoding into a Quadratic Unconstrained Binary Optimisation (QUBO) problem.

    Language:Python3100

  • Tania526-sudo/Bayesian_Network_Learning

    A Python-based implementation of Bayesian network structure learning using mutual information and Minimum Description Length (MDL) scoring. This project constructs probabilistic graphical models from real-world data and evaluates their performance in representing dependencies among variables.

    Language:Jupyter Notebook2