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Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
K. Dénes, “Gráfok és mátrixok,” Matematikai és Fizikai Lapok , vol. 38, pp. 116–119, 1931
1931
Earlier work this paper cites.
J. Edmonds, “Systems of distinct representatives and linear algebra,” Journal of Research of the Notional Bureau of Standards - B. Mathematics and Mathematical Physics , vol. 71B, no. 4, pp. 241–245, Oct-Dec 1967
1967
Earlier work this paper cites.
C. Meek, “Causal inference and causal explanation with background knowledge,” in Proc. 11th Conf. Uncertainty Artif. Intell. , 1995
1995
Earlier work this paper cites.
D. M. Chickering, “Learning Bayesian networks is NP-complete,” in Learning from Data: Artificial Intelligence and Statistics V . Springer, 1996
1996
Earlier work this paper cites.
D. P. Bertsekas, Nonlinear Programming . Belmont, MA: Athena Scientific, 1999
1999
Earlier work this paper cites.
P. Spirtes, C. N. Glymour, and R. Scheines, Causation, Prediction, and Search , 2nd ed. Cambridge, MA, USA: MIT Press, 2000
2000
Earlier work this paper cites.
M. Fazel, H. Hindi, and S. Boyd, “A rank minimization heuristic with application to minimum order system approximation,” in Proceedings of the 2001 American Control Conference , vol. 6, 2001, pp. 4734–4739
2001
Earlier work this paper cites.
——, “Optimal structure identification with greedy search,” Journal of Machine Learning Research , vol. 3, no. Nov, pp. 507–554, 2002
2002
Earlier work this paper cites.
N. Guelzim, S. Bottani, P. Bourgine, and F. Képès, “Topological and causal structure of the yeast transcriptional regulatory network,” Nature Genetics , vol. 31, pp. 60–63, 2002
2002
Earlier work this paper cites.
M. Mihail and C. Papadimitriou, “On the eigenvalue power law,” in Randomization and Approximation Techniques in Computer Science . Berlin, Heidelberg: Springer, 2002, pp. 254–262
2002
Earlier work this paper cites.
N. Srebro and T. S. Jaakkola, “Weighted low-rank approximations,” in International Conference on Machine Learning (ICML) , 2003, pp. 720–727
2003
Earlier work this paper cites.
A.-L. Barabasi and Z. N. Oltvai, “Network biology: Understanding the cell’s functional organization,” Nature Reviews Genetics , vol. 5, no. 2, pp. 101–113, 2004
2004
Earlier work this paper cites.
A. J. Hartemink, “Reverse engineering gene regulatory networks,” Nature Biotechnology , vol. 23, no. 5, pp. 554–555, 2005
2005
Earlier work this paper cites.
V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi, M. Baliki, and A. V. Apkarian, “Scale-free brain functional networks,” Physical Review Letters , vol. 94, p. 018102, Jan 2005
2005
Earlier work this paper cites.
J. Schäfer and K. Strimmer, “A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics,” Statistical Applications in Senetics and Molecular Biology , vol. 4, no. 1, 2005
2005
Earlier work this paper cites.
I. Tsamardinos, L. E. Brown, and C. F. Aliferis, “The max-min hill-climbing Bayesian network structure learning algorithm,” Machine learning , vol. 65, no. 1, pp. 31–78, 2006
2006
Earlier work this paper cites.
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen, “A linear non-Gaussian acyclic model for causal discovery,” Journal of Machine Learning Research , vol. 7, no. Oct, pp. 2003–2030, 2006
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
S. M. Fallat and L. Hogben, “The minimum rank of symmetric matrices described by a graph: a survey,” Linear Algebra and its Applications , vol. 426, no. 2-3, pp. 558–582, 2007
2007
Earlier work this paper cites.
J. Zhang, “On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias,” Artificial Intelligence , vol. 172, no. 16, pp. 1873–1896, 2008
2008
Earlier work this paper cites.
J. Pearl, Causality: Models, Reasoning and Inference . New York, NY, USA: Cambridge University Press, 2009
2009
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
K. Zhang and A. Hyvärinen, “On the identifiability of the post-nonlinear causal model,” in Uncertainty in Artificial Intelligence (UAI) , 2009
2009
Earlier work this paper cites.
V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, “Latent variable graphical model selection via convex optimization,” in 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton) , 2010
2010
Earlier work this paper cites.
L. Wu, X. Ying, and X. Wu, “Reconstruction from randomized graph via low rank approximation,” in Proceedings of the SIAM International Conference on Data Mining , 2010
2010
Earlier work this paper cites.
M. Jaggi and M. Sulovský, “A simple algorithm for nuclear norm regularized problems,” in ICML , 2010, pp. 471–478
2010
Earlier work this paper cites.
L. Hogben, “Minimum rank problems,” Linear Algebra and its Applications , vol. 432, no. 8, pp. 1961–1974, 2010
2010
Cited alongside, same era.
L. H. Mitchell, S. K. Narayan, and A. M. Zimmer, “Lower bounds in minimum rank problems,” Linear Algebra and its Applications , vol. 432, no. 1, pp. 430 – 440, 2010
2010
Cited alongside, same era.
B. Recht, “A simpler approach to matrix completion,” Journal of Machine Learning Research , vol. 12, no. 104, pp. 3413–3430, 2011
2011
Cited alongside, same era.
V. Koltchinskii, K. Lounici, and A. B. Tsybakov, “Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion,” The Annals of Statistics , vol. 39, no. 5, pp. 2302–2329, 2011
2011
Cited alongside, same era.
S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer, and K. Bollen, “Directlingam: A direct method for learning a linear non-Gaussian structural equation model,” Journal of Machine Learning Research , vol. 12, no. Apr, pp. 1225–1248, 2011
M. A. Davenport and J. Romberg, “An overview of low-rank matrix recovery from incomplete observations,” IEEE Journal of Selected Topics in Signal Processing , vol. 10, no. 4, pp. 608–622, 2016
2016
Later among the works it cites.
J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference - Foundations and Learning Algorithms . Cambridge, MA, USA: MIT Press, 2017
2017
Later among the works it cites.
J. Ramsey, M. Glymour, R. Sanchez-Romero, and C. Glymour, “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,” International Journal of Data Science and Analytics , vol. 3, no. 2, pp. 121–129, 2017
2017
Later among the works it cites.
A. Ghoshal and J. Honorio, “Learning identifiable gaussian bayesian networks in polynomial time and sample complexity,” in Advances in Neural Information Processing Systems (NIPS) , 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
C. Smith, F. Wood, and L. Paninski, “Low rank continuous-space graphical models,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2012
2012
Cited alongside, same era.
Q. Ling, Y. Xu, W. Yin, and Z. Wen, “Decentralized low-rank matrix completion,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing , 2012
2012
Cited alongside, same era.
C.-J. Hsieh, K.-Y. Chiang, and I. S. Dhillon, “Low rank modeling of signed networks,” in the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2012
2012
Cited alongside, same era.
O. Nikolova and S. Aluru, “Parallel Bayesian network structure learning with application to gene networks,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis , 2012
2012
Cited alongside, same era.
K. Zhou, H. Zha, and L. Song, “Learning social infectivity in sparse low-rank networks using multi-dimensional Hawkes processes,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2013
2013
Cited alongside, same era.
Y. Liu and A. Willsky, “Learning Gaussian graphical models with observed or latent FVSs,” in Advances in Neural Information Processing Systems , 2013
2013
Cited alongside, same era.
P. Jain, P. Netrapalli, and S. Sanghavi, “Low-rank matrix completion using alternating minimization,” in Proceedings of the Forty-Fifth Annual ACM Symposium on Theory of Computing , 2013
2013
Cited alongside, same era.
J. Zhang, J. Chen, S. Zhi, Y. Chang, P. Yu, and J. Han, “Link prediction across aligned networks with sparse and low rank matrix estimation,” in IEEE 33rd International Conference on Data Engineering , 2017
2017
Later among the works it cites.
S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, and L. Zhang, “Weighted nuclear norm minimization and its applications to low level vision,” International Journal of Computer Vision , vol. 121, no. 2, pp. 183–208, 2017
2017
Later among the works it cites.
X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing, “DAGs with NO TEARS: Continuous optimization for structure learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018
2018
Later among the works it cites.
——, “Learning linear structural equation models in polynomial time and sample complexity,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2018
2018
Later among the works it cites.
P. Tichavskỳ and J. Vomlel, “Representations of bayesian networks by low-rank models,” in International Conference on Probabilistic Graphical Models , 2018
2018
Later among the works it cites.
M. Kocaoglu, C. Snyder, A. G. Dimakis, and S. Vishwanath, “CausalGAN: Learning causal implicit generative models with adversarial training,” in International Conference on Learning Representations , 2018
2018
Later among the works it cites.
Y. Yu, J. Chen, T. Gao, and M. Yu, “DAG-GNN: DAG structure learning with graph neural networks,” in International Conference on Machine Learning (ICML) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
B. Frot, P. Nandy, and M. H. Maathuis, “Robust causal structure learning with some hidden variables,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 81, no. 3, pp. 459–487, 2019
2019
Later among the works it cites.
A. Barik and J. Honorio, “Learning Bayesian networks with low rank conditional probability tables,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
2019
Later among the works it cites.
Y. Chi, Y. M. Lu, and Y. Chen, “Nonconvex optimization meets low-rank matrix factorization: An overview,” IEEE Transactions on Signal Processing , vol. 67, no. 20, pp. 5239–5269, 2019
2019
Later among the works it cites.
S. Lachapelle, P. Brouillard, T. Deleu, and S. Lacoste-Julien, “Gradient-based neural DAG learning,” in International Conference on Learning Representations (ICLR) , 2020
2020
Closest in time.
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. P. Xing, “Learning sparse nonparametric DAGs,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2020
2020
Closest in time.
S. Zhu, I. Ng, and Z. Chen, “Causal discovery with reinforcement learning,” in International Conference on Learning Representations (ICLR) , 2020
2020
Closest in time.
I. Ng, A. Ghassami, and K. Zhang, “On the role of sparsity and dag constraints for learning linear dags,” in Advances in Neural Information Processing Systems (NeurIPS) , 2020
2020
Closest in time.
X. Wang, Y. Du, S. Zhu, L. Ke, Z. Chen, J. Hao, and J. Wang, “Ordering-based causal discovery with reinforcement learning,” in International Joint Conferences on Artificial Intelligence (IJCAI) , 2021
2021
Closest in time.
2021
Closest in time.
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio, “Toward causal representation learning,” Proceedings of the IEEE , vol. 109, no. 5, pp. 612–634, 2021
2021
Closest in time.
I. Ng, S. Zhu, Z. Fang, H. Li, Z. Chen, and J. Wang, “Masked gradient-based causal structure learning,” in the SIAM International Conference on Data Mining (SDM) , 2022
2022
Closest in time.
D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, and M. Sebag, “Structural agnostic modeling: Adversarial learning of causal graphs,” Journal of Machine Learning Research , vol. 23, no. 219, pp. 1–62, 2022
2022
Closest in time.
B. Schölkopf, Causality for Machine Learning , 1st ed. New York, NY, USA: Association for Computing Machinery, 2022, p. 765–804
2022
Closest in time.