ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2107.01658
  4. Cited By
Learning Bayesian Networks through Birkhoff Polytope: A Relaxation
  Method

Learning Bayesian Networks through Birkhoff Polytope: A Relaxation Method

4 July 2021
Aramayis Dallakyan
Mohsen Pourahmadi
    CML
ArXivPDFHTML

Papers citing "Learning Bayesian Networks through Birkhoff Polytope: A Relaxation Method"

17 / 17 papers shown
Title
Learning Sparse Nonparametric DAGs
Learning Sparse Nonparametric DAGs
Xun Zheng
Chen Dan
Bryon Aragam
Pradeep Ravikumar
Eric Xing
CML
153
260
0
29 Sep 2019
Optimizing regularized Cholesky score for order-based learning of
  Bayesian networks
Optimizing regularized Cholesky score for order-based learning of Bayesian networks
Qiaoling Ye
Arash A. Amini
Qing Zhou
BDL
CML
44
30
0
28 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
96
941
0
04 Mar 2018
BigVAR: Tools for Modeling Sparse High-Dimensional Multivariate Time
  Series
BigVAR: Tools for Modeling Sparse High-Dimensional Multivariate Time Series
William B. Nicholson
David S. Matteson
Jacob Bien
37
19
0
23 Feb 2017
Learning Local Dependence In Ordered Data
Learning Local Dependence In Ordered Data
Guo Yu
Jacob Bien
110
34
0
25 Apr 2016
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
Bryon Aragam
Qing Zhou
CML
112
107
0
04 Jan 2014
High-dimensional learning of linear causal networks via inverse
  covariance estimation
High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh
Peter Buhlmann
CML
115
189
0
14 Nov 2013
Regularized M-estimators with nonconvexity: Statistical and algorithmic
  theory for local optima
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Po-Ling Loh
Martin J. Wainwright
280
517
0
10 May 2013
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
105
3,981
0
27 Feb 2013
Ordering-Based Search: A Simple and Effective Algorithm for Learning
  Bayesian Networks
Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks
M. Teyssier
D. Koller
142
374
0
04 Jul 2012
A simple approach for finding the globally optimal Bayesian network
  structure
A simple approach for finding the globally optimal Bayesian network structure
T. Silander
P. Myllymäki
TPM
90
398
0
27 Jun 2012
Advances in exact Bayesian structure discovery in Bayesian networks
Advances in exact Bayesian structure discovery in Bayesian networks
Mikko Koivisto
TPM
60
90
0
27 Jun 2012
Large Vector Auto Regressions
Large Vector Auto Regressions
Song Song
Peter J. Bickel
AI4TS
137
183
0
20 Jun 2011
Extended Bayesian Information Criteria for Gaussian Graphical Models
Extended Bayesian Information Criteria for Gaussian Graphical Models
Rina Foygel
Mathias Drton
167
869
0
30 Nov 2010
Nearly unbiased variable selection under minimax concave penalty
Nearly unbiased variable selection under minimax concave penalty
Cun-Hui Zhang
332
3,564
0
25 Feb 2010
Penalized Likelihood Methods for Estimation of Sparse High Dimensional
  Directed Acyclic Graphs
Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs
Ali Shojaie
George Michailidis
CML
137
200
0
28 Nov 2009
A path following algorithm for the graph matching problem
A path following algorithm for the graph matching problem
M. Zaslavskiy
Francis R. Bach
Jean-Philippe Vert
138
422
0
23 Jan 2008
1