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Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.
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A bayesian approach to causal discovery
David Heckerman, Christopher Meek, and Gregory Cooper · 2006
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An application of bayesian network for predicting object-oriented software maintainability
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Improving the structure mcmc sampler for bayesian networks by introducing a new edge reversal move
Marco Grzegorczyk and Dirk Husmeier · 2008
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Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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Causality
Judea Pearl · 2009
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Numerical solution of stochastic differential equations with jumps in finance
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Daniel Eaton and Kevin Murphy · 2012
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Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Mikko Koivisto · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Variational implicit processes
Chao Ma, Yingzhen Li, and José Miguel Hernández-Lobato · 2019
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Functional variational bayesian neural networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse · 2019
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Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
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Fast differentiable sorting and ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga · 2020
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Sliced kernelized stein discrepancy
Wenbo Gong, Yingzhen Li, and José Miguel Hernández-Lobato · 2020
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Informed proposals for local mcmc in discrete spaces
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Diederik P Kingma and Jimmy Ba · 2014
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Addendum on the scoring of gaussian directed acyclic graphical models
Jack Kuipers, Giusi Moffa, and David Heckerman · 2014
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Identifiability of gaussian structural equation models with equal error variances
Jonas Peters and Peter Bühlmann · 2014
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Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
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On the convergence of stochastic gradient mcmc algorithms with high-order integrators
Changyou Chen, Nan Ding, and Lawrence Carin · 2015
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A complete recipe for stochastic gradient mcmc
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Preconditioned stochastic gradient langevin dynamics for deep neural networks
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Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2021
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Foundations of structural causal models with cycles and latent variables
Stephan Bongers, Patrick Forré, Jonas Peters, and Joris M Mooij · 2021
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Bcd nets: Scalable variational approaches for bayesian causal discovery
Chris Cundy, Aditya Grover, and Stefano Ermon · 2021
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Active slices for sliced stein discrepancy
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Dibs: Differentiable bayesian structure learning
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Bertrand Charpentier, Simon Kibler, and Stephan Günnemann · 2022
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Deep end-to-end causal inference
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Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy
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Rhino: Deep causal temporal relationship learning with history-dependent noise
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Discrete langevin sampler via wasserstein gradient flow
Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, and Dale Schuurmans · 2022
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Interventions, where and how? experimental design for causal models at scale
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, and Stefan Bauer · 2022
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A langevin-like sampler for discrete distributions
Ruqi Zhang, Xingchao Liu, and Qiang Liu · 2022
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Differentiable multi-target causal bayesian experimental design
Yashas Annadani, Panagiotis Tigas, Desi R Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, and Stefan Bauer · 2023
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Dag learning on the permutahedron
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