Structure Learning with Continuous Optimization: A Sober Look and Beyond

4 April 2023

Papers citing "Structure Learning with Continuous Optimization: A Sober Look and Beyond"

47 / 47 papers shown

Title
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 79 1 0 21 Mar 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery Yingyu Lin Yuxing Huang Wenqin Liu Haoran Deng Ignavier Ng Kun Zhang Biwei Huang Yi-An Ma Zhen Zhang 68 1 0 08 Oct 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models Menghua Wu Yujia Bao Regina Barzilay Tommi Jaakkola CML 81 7 0 02 Feb 2024
Causal-learn: Causal Discovery in Python Yujia Zheng Erdun Gao Wei Chen Joseph Ramsey Biwei Huang Ruichu Cai Shohei Shimizu Peter Spirtes Kun Zhang CML 61 69 0 31 Jul 2023
Global Optimality in Bivariate Gradient-based DAG Learning Chang Deng Kevin Bello Bryon Aragam Pradeep Ravikumar 54 8 0 30 Jun 2023
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization Kevin Bello Bryon Aragam Pradeep Ravikumar 91 83 0 16 Sep 2022
On the Sparse DAG Structure Learning Based on Adaptive Lasso Danru Xu Erdun Gao Wei Huang Menghan Wang Andy Song Biwei Huang CML 57 4 0 07 Sep 2022
Truncated Matrix Power Iteration for Differentiable DAG Learning Zhen Zhang Ignavier Ng Dong Gong Yuhang Liu Ehsan Abbasnejad Biwei Huang Kun Zhang Javen Qinfeng Shi 49 25 0 30 Aug 2022
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education Wenbo Gong Digory Smith Zichao Wang Craig Barton Simon Woodhead Nick Pawlowski Joel Jennings Cheng Zhang CML 41 4 0 17 Aug 2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Erdun Gao Ignavier Ng Biwei Huang Li Shen Wei Huang Tongliang Liu Kun Zhang H. Bondell CML 105 23 0 27 May 2022
Differentiable DAG Sampling Bertrand Charpentier Simon Kibler Stephan Günnemann 68 42 0 16 Mar 2022
Differentiable Causal Discovery Under Latent Interventions Gonccalo R. A. Faria André F. T. Martins Mário A. T. Figueiredo BDL CML OOD 67 23 0 04 Mar 2022
FedDAG: Federated DAG Structure Learning Erdun Gao Junjia Chen Li Shen Tongliang Liu Biwei Huang H. Bondell FedML 62 17 0 07 Dec 2021
Multi-task Learning of Order-Consistent Causal Graphs Xinshi Chen Haoran Sun Caleb N. Ellington Eric Xing Le Song CML 49 15 0 03 Nov 2021
Towards Federated Bayesian Network Structure Learning with Continuous Optimization Ignavier Ng Kun Zhang FedML 59 38 0 18 Oct 2021
DAGs with No Curl: An Efficient DAG Structure Learning Approach Yue Yu Tian Gao Naiyu Yin Q. Ji CML 49 60 0 14 Jun 2021
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 79 65 0 12 Apr 2021
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding Alexis Bellot M. Schaar CML 29 11 0 28 Mar 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 89 300 0 03 Mar 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game Alexander G. Reisach C. Seiler S. Weichwald CML 56 141 0 26 Feb 2021
On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng Sébastien Lachapelle Nan Rosemary Ke Simon Lacoste-Julien Kun Zhang 68 38 0 23 Nov 2020
Learning causal representations for robust domain adaptation shuai Yang Kui Yu Fuyuan Cao Lin Liu Hongya Wang Jiuyong Li OOD CML TTA 44 44 0 12 Nov 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis L. Wei Tian Gao Yue Yu CML 72 71 0 18 Oct 2020
Differentiable Causal Discovery Under Unmeasured Confounding Rohit Bhattacharya Tushar Nagarajan Daniel Malinsky I. Shpitser CML 58 60 0 14 Oct 2020
Causal Discovery with Multi-Domain LiNGAM for Latent Factors Yan Zeng Shohei Shimizu Ruichu Cai Feng Xie Michio Yamamoto Zhifeng Hao CML 46 21 0 19 Sep 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 56 186 0 03 Jul 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 50 189 0 17 Jun 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 64 191 0 02 Feb 2020
Causal Discovery from Incomplete Data: A Deep Learning Approach Yuhao Wang Vlado Menkovski Hao Wang Xin Du Mykola Pechenizkiy CML 53 34 0 15 Jan 2020
Masked Gradient-Based Causal Structure Learning Ignavier Ng Shengyu Zhu Zhuangyan Fang Haoyang Li Zhitang Chen Jun Wang CML 100 117 0 18 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric Xing CML 150 260 0 29 Sep 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 50 273 0 05 Jun 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 67 485 0 22 Apr 2019
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement W. Kool H. V. Hoof Max Welling 109 220 0 14 Mar 2019
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan Olivier Goudet Isabelle M Guyon David Lopez-Paz Michèle Sebag CML 49 94 0 13 Mar 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 79 937 0 04 Mar 2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks Gonzalo E. Mena David Belanger Scott W. Linderman Jasper Snoek 72 270 0 23 Feb 2018
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.4K 149,842 0 22 Dec 2014
Support recovery without incoherence: A case for nonconvex regularization Po-Ling Loh Martin J. Wainwright 149 169 0 17 Dec 2014
High-dimensional learning of linear causal networks via inverse covariance estimation Po-Ling Loh Peter Buhlmann CML 95 189 0 14 Nov 2013
Large-Sample Learning of Bayesian Networks is NP-Hard D. M. Chickering Christopher Meek David Heckerman BDL 111 793 0 19 Oct 2012
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 167 336 0 11 May 2012
Bayesian network learning with cutting planes James Cussens 56 257 0 14 Feb 2012
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 97 510 0 13 Jan 2011
Nearly unbiased variable selection under minimax concave penalty Cun-Hui Zhang 311 3,557 0 25 Feb 2010
$High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence$ High-dimensional covariance estimation by minimizing $\ell_1$ -penalized log-determinant divergence Pradeep Ravikumar Martin J. Wainwright Garvesh Raskutti Bin Yu 229 874 0 21 Nov 2008
Statistical ranking and combinatorial Hodge theory Xiaoye Jiang Lek-Heng Lim Yuan Yao Yinyu Ye 77 372 0 07 Nov 2008