Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

27 February 2013

Papers citing "Learning Bayesian Networks: The Combination of Knowledge and Statistical Data"

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Title
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing Ning Xu Timothy C. G. Fisher Jian Hong 13 0 0 30 Jul 2020
Accuracy and stability of solar variable selection comparison under complicated dependence structures Ning Xu Timothy C. G. Fisher Jian Hong 11 0 0 30 Jul 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 36 57 0 21 Jul 2020
Moment-Matching Graph-Networks for Causal Inference M. Park CML BDL 21 0 0 20 Jul 2020
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks Alessandro Bregoli M. Scutari Fabio Stella CML 17 10 0 07 Jul 2020
Solving Bayesian Network Structure Learning Problem with Integer Linear Programming Ronald Seoh 6 1 0 06 Jul 2020
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning R. VaidyanathanP. Stefan Szeider BDL 6 19 0 24 Jun 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 8 189 0 17 Jun 2020
Causal inference of brain connectivity from fMRI with $ψ$ -Learning Incorporated Linear non-Gaussian Acyclic Model ( $ψ$ -LiNGAM) Aiying Zhang Gemeng Zhang Biao Cai Wenxing Hu Li Xiao T. Wilson Julia M. Stephen Vince D. Calhoun Yu-Ping Wang 10 0 0 16 Jun 2020
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence Aiying Zhang Gemeng Zhang Cai Biao T. Wilson Julia M. Stephen Vince D. Calhoun Yu-Ping Wang 8 0 0 16 Jun 2020
Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets Zhi-gao Guo Anthony C. Constantinou 14 7 0 08 Jun 2020
Learning DAGs without imposing acyclicity Gherardo Varando CML 22 12 0 04 Jun 2020
An Analysis of the Adaptation Speed of Causal Models Rémi Le Priol Reza Babanezhad Harikandeh Yoshua Bengio Simon Lacoste-Julien CML 17 14 0 18 May 2020
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data Anthony C. Constantinou Yang Liu Kiattikun Chobtham Zhi-gao Guo N. K. Kitson CML 27 61 0 18 May 2020
Learning Adjustment Sets from Observational and Limited Experimental Data Sofia Triantafillou Gregory F. Cooper CML 6 6 0 18 May 2020
A Robust Experimental Evaluation of Automated Multi-Label Classification Methods A. G. C. D. Sá C. Pimenta G. Pappa A. Freitas 11 7 0 16 May 2020
Learning Bayesian Networks from Incomplete Data with the Node-Average Likelihood T. Bodewes M. Scutari 16 6 0 29 Apr 2020
Causal network learning with non-invertible functional relationships Bingling Wang Qing Zhou CML 6 6 0 20 Apr 2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder Mengyue Yang Furui Liu Zhitang Chen Xinwei Shen Jianye Hao Jun Wang OOD CoGe CML 26 44 0 18 Apr 2020
Learning Bayesian Networks that enable full propagation of evidence Anthony C. Constantinou 19 17 0 09 Apr 2020
A Critique on the Interventional Detection of Causal Relationships Mehrzad Saremi CML 9 0 0 26 Mar 2020
Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world B. Butcher V. Huang Jeremy Reffin S. Sgaier Grace Charles Novi Quadrianto CML 24 17 0 12 Mar 2020
DYNOTEARS: Structure Learning from Time-Series Data Roxana Pamfil Nisara Sriwattanaworachai Shaan Desai Philip Pilgerstorfer Paul Beaumont K. Georgatzis Bryon Aragam CML AI4TS BDL 17 187 0 02 Feb 2020
Additive Bayesian Network Modelling with the R Package abn Gilles Kratzer F. Lewis A. Comin M. Pittavino Reinhard Furrer CML 12 14 0 20 Nov 2019
A Graph Autoencoder Approach to Causal Structure Learning Ignavier Ng Shengyu Zhu Zhitang Chen Zhuangyan Fang BDL CML 8 81 0 18 Nov 2019
Causality-based Feature Selection: Methods and Evaluations Kui Yu Xianjie Guo Lin Liu Jiuyong Li Hao Wang Zhaolong Ling Xindong Wu CML 16 92 0 17 Nov 2019
Bayesian causal inference via probabilistic program synthesis Sam Witty Alexander K. Lew David D. Jensen Vikash K. Mansinghka 9 3 0 30 Oct 2019
Learning pairwise Markov network structures using correlation neighborhoods Juri Kuronen J. Corander J. Pensar 13 0 0 30 Oct 2019
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs AmirEmad Ghassami Alan Yang Negar Kiyavash Kun Zhang 21 2 0 28 Oct 2019
Learning from both experts and data R. Besson E. L. Pennec S. Allassonnière 8 4 0 20 Oct 2019
Interventional Experiment Design for Causal Structure Learning AmirEmad Ghassami Saber Salehkaleybar Negar Kiyavash CML 12 9 0 12 Oct 2019
Learning Neural Causal Models from Unknown Interventions Nan Rosemary Ke O. Bilaniuk Anirudh Goyal Stefan Bauer Hugo Larochelle Bernhard Schölkopf Michael C. Mozer C. Pal Yoshua Bengio CML OOD 23 168 0 02 Oct 2019
Bayesian Network Models for Incomplete and Dynamic Data M. Scutari SyDa 10 0 0 15 Jun 2019
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections R. Y. Rohekar Yaniv Gurwicz Shami Nisimov Gal Novik BDL UQCV 14 13 0 30 May 2019
Evaluating structure learning algorithms with a balanced scoring function Anthony C. Constantinou CML 6 18 0 29 May 2019
Bayesian Learning of Sum-Product Networks Martin Trapp Robert Peharz Hong Ge Franz Pernkopf Zoubin Ghahramani TPM 11 50 0 26 May 2019
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Biwei Huang Kun Zhang Mingming Gong Clark Glymour CML AI4TS 19 63 0 26 May 2019
Learning Gaussian DAGs from Network Data Hangjian Li Oscar Hernan Madrid Padilla Qing Zhou CML 15 2 0 26 May 2019
Invoice Financing of Supply Chains with Blockchain technology and Artificial Intelligence S. Johnson Peter Robinson Kishore Atreya Claudio Lisco 13 1 0 25 May 2019
On Pruning for Score-Based Bayesian Network Structure Learning Alvaro H. C. Correia James Cussens Cassio de Campos 13 14 0 23 May 2019
Optimizing regularized Cholesky score for order-based learning of Bayesian networks Qiaoling Ye Arash A. Amini Qing Zhou BDL CML 20 28 0 28 Apr 2019
Learning big Gaussian Bayesian networks: partition, estimation, and fusion J. Gu Qing Zhou GNN 14 19 0 24 Apr 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 8 476 0 22 Apr 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 22 626 0 29 Mar 2019
Time Series Imputation Samuel Arcadinho P. Mateus AI4TS 17 2 0 22 Mar 2019
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns A. Cemgil M. Burak Kurutmaz S. Yıldırım Melih Barsbey Umut Simsekli 13 6 0 11 Mar 2019
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes Biwei Huang Kun Zhang Jiji Zhang Joseph Ramsey Ruben Sanchez-Romero Clark Glymour Bernhard Schölkopf 13 219 0 05 Mar 2019
Learning Factored Markov Decision Processes with Unawareness Craig Innes A. Lascarides 13 4 0 27 Feb 2019
On resampling vs. adjusting probabilistic graphical models in estimation of distribution algorithms Mohamed El Yafrani M. Martins M. Delgado Inkyung Sung R. Lüders Markus Wagner TPM 12 0 0 15 Feb 2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio T. Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle O. Bilaniuk Anirudh Goyal C. Pal CML OOD 30 332 0 30 Jan 2019