Fetching the paper…
Reading the bibliography…
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales superexponentially with the number of nodes.
On the number of cycles in a graph
F. Harary and B. Manvel · 1971
Earlier work this paper cites.
Counting unlabeled acyclic digraphs
R. W. Robinson · 1977
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
P. Spirtes and C. Glymour · 1991
Earlier work this paper cites.
Probabilistic network construction using the minimum description length principle
R. R. Bouckaert · 1993
Earlier work this paper cites.
A limited memory algorithm for bound constrained optimization
R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu · 1995
Earlier work this paper cites.
Learning Bayesian networks: The combination of knowledge and statistical data
D. Heckerman, D. Geiger, and D. M. Chickering · 1995
Earlier work this paper cites.
Learning Bayesian networks is NP-complete
D. M. Chickering · 1996
Earlier work this paper cites.
Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
D. M. Chickering and D. Heckerman · 1997
Earlier work this paper cites.
Collective dynamics of small-world networks
D. J. Watts and S. H. Strogatz · 1998
Earlier work this paper cites.
Emergence of scaling in random networks
A.-L. Barabási and R. Albert · 1999
Earlier work this paper cites.
Optimization II: Standard Numerical Methods for Nonlinear Continuous Optimization
A. Nemirovski · 1999
Earlier work this paper cites.
Causation, prediction, and search , volume 81
P. Spirtes, C. Glymour, and R. Scheines · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
D. M. Chickering · 2003
Earlier work this paper cites.
Finding optimal gene networks using biological constraints
S. Ott and S. Miyano · 2003
Earlier work this paper cites.
Large-sample learning of Bayesian networks is NP-hard
D. M. Chickering, D. Heckerman, and C. Meek · 2004
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Y. Nesterov · 2005
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
K. Sachs, O. Perez, D. Pe’er, D. A. Lauffenburger, and G. P. Nolan · 2005
Earlier work this paper cites.
Finding optimal bayesian networks by dynamic programming
A. P. Singh and A. W. Moore · 2005
Earlier work this paper cites.
Ordering-based search: A simple and effective algorithm for learning bayesian networks
M. Teyssier and D. Koller · 2005
Earlier work this paper cites.
Numerical Optimization
J. Nocedal and S. J. Wright · 2006
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen · 2006
Cited alongside, same era.
A simple approach for finding the globally optimal bayesian network structure
T. Silander and P. Myllymaki · 2006
Cited alongside, same era.
The max-min hill-climbing Bayesian network structure learning algorithm
I. Tsamardinos, L. E. Brown, and C. F. Aliferis · 2006
Cited alongside, same era.
Learning graphical model structure using L1-regularization paths
M. Schmidt, A. Niculescu-Mizil, and K. Murphy · 2007
Cited alongside, same era.
Model selection and estimation in the Gaussian graphical model
M. Yuan and Y. Lin · 2007
Cited alongside, same era.
Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data
O. Banerjee, L. El Ghaoui, and A. d’Aspremont · 2008
CVX: Matlab software for disciplined convex programming, version 2.1
M. Grant and S. Boyd · 2014
Later among the works it cites.
Quic: quadratic approximation for sparse inverse covariance estimation
C.-J. Hsieh, M. A. Sustik, I. S. Dhillon, and P. Ravikumar · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Later among the works it cites.
Addendum on the scoring of gaussian directed acyclic graphical models
J. Kuipers, G. Moffa, and D. Heckerman · 2014
Later among the works it cites.
High-dimensional learning of linear causal networks via inverse covariance estimation
P.-L. Loh and P. Bühlmann · 2014
Later among the works it cites.
Proximal quasi-newton for computationally intensive l1-regularized m-estimators
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The tradeoffs of large scale learning
O. Bousquet and L. Bottou · 2008
Cited alongside, same era.
Learning causal Bayesian network structures from experimental data
B. Ellis and W. H. Wong · 2008
Cited alongside, same era.
Sparse inverse covariance estimation with the Graphical Lasso
J. Friedman, T. Hastie, and R. Tibshirani · 2008
Cited alongside, same era.
A New Scaling and Squaring Algorithm for the Matrix Exponential
A. H. Al-Mohy and N. J. Higham · 2009
Cited alongside, same era.
Probabilistic graphical models: principles and techniques
D. Koller and N. Friedman · 2009
Cited alongside, same era.
Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm
M. Schmidt, E. Berg, M. Friedlander, and K. Murphy · 2009
Cited alongside, same era.
K. Zhong, I. E.-H. Yen, I. S. Dhillon, and P. K. Ravikumar · 2014
Later among the works it cites.
Concave penalized estimation of sparse Gaussian Bayesian networks
B. Aragam and Q. Zhou · 2015
Later among the works it cites.
Learning bayesian networks with thousands of variables
M. Scanagatta, C. P. de Campos, G. Corani, and M. Zaffalon · 2015
Later among the works it cites.
Machine learning of bayesian networks using constraint programming
P. Van Beek and H.-F. Hoffmann · 2015
Later among the works it cites.
Learning directed acyclic graphs with penalized neighbourhood regression
B. Aragam, A. A. Amini, and Q. Zhou · 2016
Later among the works it cites.
Optimization methods for large-scale machine learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2016
Later among the works it cites.
Learning bayesian networks with ancestral constraints
E. Y.-J. Chen, Y. Shen, A. Choi, and A. Darwiche · 2016
Later among the works it cites.
Structure discovery in bayesian networks by sampling partial orders
T. Niinimäki, P. Parviainen, and M. Koivisto · 2016
Later among the works it cites.
A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
J. Ramsey, M. Glymour, R. Sanchez-Romero, and C. Glymour · 2016
Later among the works it cites.
Learning treewidth-bounded bayesian networks with thousands of variables
M. Scanagatta, G. Corani, C. P. de Campos, and M. Zaffalon · 2016
Later among the works it cites.
Training neural networks without gradients: A scalable admm approach
G. Taylor, R. Burmeister, Z. Xu, B. Singh, A. Patel, and T. Goldstein · 2016
Later among the works it cites.
No penalty no tears: Least squares in high-dimensional linear models
X. Wang, D. Dunson, and C. Leng · 2016
Later among the works it cites.
Practical gauss-newton optimisation for deep learning
A. Botev, H. Ritter, and D. Barber · 2017
Later among the works it cites.
Polyhedral aspects of score equivalence in bayesian network structure learning
J. Cussens, D. Haws, and M. Studenỳ · 2017
Later among the works it cites.
Penalized estimation of directed acyclic graphs from discrete data
J. Gu, F. Fu, and Q. Zhou · 2018
Closest in time.