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DiBS: Differentiable Bayesian Structure Learning

25 May 2021

Papers citing "DiBS: Differentiable Bayesian Structure Learning"

35 / 35 papers shown

Title
Causal Temporal Regime Structure Learning Abdellah Rahmani Pascal Frossard CML 235 2 0 20 Feb 2025
Amortized Inference of Causal Models via Conditional Fixed-Point Iterations Divyat Mahajan Jannes Gladrow Agrin Hilmkil Cheng Zhang M. Scetbon 113 2 0 08 Oct 2024
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes Georg Manten Cecilia Casolo E. Ferrucci Søren Wengel Mogensen C. Salvi Niki Kilbertus CML BDL 203 12 0 28 Feb 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 112 1 0 22 Feb 2024
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions D. Geiger David Heckerman 137 195 0 05 May 2021
On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng Sébastien Lachapelle Nan Rosemary Ke Simon Lacoste-Julien Kun Zhang 83 38 0 23 Nov 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis L. Wei Tian Gao Yue Yu CML 83 71 0 18 Oct 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 66 189 0 03 Jul 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 57 190 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 77 192 0 02 Feb 2020
A Graph Autoencoder Approach to Causal Structure Learning Ignavier Ng Shengyu Zhu Zhitang Chen Zhuangyan Fang BDL CML 60 83 0 18 Nov 2019
Masked Gradient-Based Causal Structure Learning Ignavier Ng Shengyu Zhu Zhuangyan Fang Haoyang Li Zhitang Chen Jun Wang CML 121 117 0 18 Oct 2019
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks Julius von Kügelgen Paul Kishan Rubenstein Bernhard Schölkopf Adrian Weller CML 57 20 0 09 Oct 2019
Learning Neural Causal Models from Unknown Interventions Nan Rosemary Ke O. Bilaniuk Anirudh Goyal Stefan Bauer Hugo Larochelle Bernhard Schölkopf Michael C. Mozer C. Pal Yoshua Bengio CML OOD 107 169 0 02 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric Xing CML 165 260 0 29 Sep 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 82 415 0 25 Jun 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 64 275 0 05 Jun 2019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang Shali Jiang Zhicheng Cui Roman Garnett Yixin Chen GNN BDL CML 105 202 0 24 Apr 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 82 489 0 22 Apr 2019
Causal Discovery Toolbox: Uncover causal relationships in Python Diviyan Kalainathan Olivier Goudet CML 51 82 0 06 Mar 2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio T. Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle O. Bilaniuk Anirudh Goyal C. Pal CML OOD 117 334 0 30 Jan 2019
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan Olivier Goudet Isabelle M Guyon David Lopez-Paz Michèle Sebag CML 58 95 0 13 Mar 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 102 949 0 04 Mar 2018
Variational Graph Auto-Encoders Thomas Kipf Max Welling GNN BDL SSL CML 153 3,592 0 21 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 198 2,537 0 02 Nov 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 80 1,094 0 16 Aug 2016
Partition MCMC for inference on acyclic digraphs Jack Kuipers G. Moffa 123 92 0 20 Apr 2015
Addendum on the scoring of Gaussian directed acyclic graphical models Jack Kuipers G. Moffa David Heckerman 144 71 0 27 Feb 2014
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs Alain Hauser Peter Buhlmann CML 90 95 0 13 Mar 2013
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman D. Geiger D. M. Chickering TPM 119 3,983 0 27 Feb 2013
Learning Gaussian Networks D. Geiger David Heckerman TPM 81 500 0 27 Feb 2013
Data Analysis with Bayesian Networks: A Bootstrap Approach N. Friedman M. Goldszmidt A. Wyner TPM 89 377 0 23 Jan 2013
Advances in exact Bayesian structure discovery in Bayesian networks Mikko Koivisto TPM 73 91 0 27 Jun 2012
Bayesian structure learning using dynamic programming and MCMC Daniel Eaton Kevin P. Murphy 96 136 0 20 Jun 2012
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 196 339 0 11 May 2012