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Efficient and Scalable Structure Learning for Bayesian Networks:
  Algorithms and Applications

Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications

7 December 2020
Rong Zhu
A. Pfadler
Ziniu Wu
Yuxing Han
Xiaoke Yang
Feng Ye
Zhenping Qian
Jingren Zhou
Tengjiao Wang
ArXivPDFHTML

Papers citing "Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications"

15 / 15 papers shown
Title
Scaling structural learning with NO-BEARS to infer causal transcriptome
  networks
Scaling structural learning with NO-BEARS to infer causal transcriptome networks
Hao-Chih Lee
M. Danieletto
Riccardo Miotto
S. Cherng
J. Dudley
CML
24
45
0
31 Oct 2019
Learning Sparse Nonparametric DAGs
Learning Sparse Nonparametric DAGs
Xun Zheng
Chen Dan
Bryon Aragam
Pradeep Ravikumar
Eric Xing
CML
131
257
0
29 Sep 2019
Causal Discovery with Reinforcement Learning
Causal Discovery with Reinforcement Learning
Shengyu Zhu
Ignavier Ng
Zhitang Chen
CML
30
239
0
11 Jun 2019
Gradient-Based Neural DAG Learning
Gradient-Based Neural DAG Learning
Sébastien Lachapelle
P. Brouillard
T. Deleu
Simon Lacoste-Julien
BDL
CML
29
270
0
05 Jun 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu
Jie Chen
Tian Gao
Mo Yu
BDL
CML
GNN
58
481
0
22 Apr 2019
DAGs with NO TEARS: Continuous Optimization for Structure Learning
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng
Bryon Aragam
Pradeep Ravikumar
Eric Xing
NoLa
CML
OffRL
52
925
0
04 Mar 2018
Bayesian Network Learning via Topological Order
Bayesian Network Learning via Topological Order
Young Woong Park
Diego Klabjan
TPM
33
29
0
20 Jan 2017
Structure Learning in Graphical Modeling
Structure Learning in Graphical Modeling
Mathias Drton
Marloes H. Maathuis
CML
64
246
0
07 Jun 2016
No penalty no tears: Least squares in high-dimensional linear models
No penalty no tears: Least squares in high-dimensional linear models
Xiangyu Wang
David B. Dunson
Chenlei Leng
81
15
0
07 Jun 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
535
149,474
0
22 Dec 2014
Penalized Estimation of Directed Acyclic Graphs From Discrete Data
Penalized Estimation of Directed Acyclic Graphs From Discrete Data
J. Gu
Fei Fu
Qing Zhou
CML
50
42
0
10 Mar 2014
Addendum on the scoring of Gaussian directed acyclic graphical models
Addendum on the scoring of Gaussian directed acyclic graphical models
Jack Kuipers
G. Moffa
David Heckerman
109
70
0
27 Feb 2014
Learning Bayesian Networks: The Combination of Knowledge and Statistical
  Data
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
David Heckerman
D. Geiger
D. M. Chickering
TPM
93
3,980
0
27 Feb 2013
A Graphical Model Formulation of Collaborative Filtering Neighbourhood
  Methods with Fast Maximum Entropy Training
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Aaron Defazio
T. Caetano
GNN
337
14
0
18 Jun 2012
DirectLiNGAM: A direct method for learning a linear non-Gaussian
  structural equation model
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu
Takanori Inazumi
Yasuhiro Sogawa
Aapo Hyvarinen
Yoshinobu Kawahara
Takashi Washio
P. Hoyer
K. Bollen
CML
61
503
0
13 Jan 2011
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