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Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.
A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2009
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Causality
Judea Pearl · 2009
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Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables
Robert E. Tillman and Peter Spirtes · 2011
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvarinen · 2012
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Learning bayesian networks: The combination of knowledge and statistical data
David Heckerman, Dan Geiger, and David Maxwell Chickering · 2013
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Equitability, mutual information, and the maximal information coefficient
Justin B Kinney and Gurinder S Atwal · 2014
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Deep convolutional inverse graphics network
Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Causal inference using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain
Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, editors · 2016
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Learning independent features with adversarial nets for non-linear ica
Philemon Brakel and Yoshua Bengio · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Unsupervised learning of disentangled and interpretable representations from sequential data
Wei-Ning Hsu, Yu Zhang, and James Glass · 2017
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Causalgan: Learning causal implicit generative models with adversarial training
Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, and Sriram Vishwanath · 2017
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Elements of causal inference
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Raphael Suter, Dorde Miladinović, Bernhard Schölkopf, and Stefan Bauer · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Learning neural causal models from unknown interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, and Yoshua Bengio · 2019
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Variational autoencoders and nonlinear ICA: A unifying framework
Ilyes Khemakhem, Diederik P. Kingma, and Aapo Hyvärinen · 2019
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Counterfactuals uncover the modular structure of deep generative models
Michel Besserve, Rémy Sun, and Bernhard Schölkopf · 2018
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Understanding disentangling in
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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Learning to decompose and disentangle representations for video prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles · 2018
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Hyunjik Kim and Andriy Mnih · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
Cited alongside, same era.
Disentangling disentanglement in variational autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh · 2018
Cited alongside, same era.
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, and Olivier Bachem · 2019
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Learning disentangled representations for recommendation
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu · 2019
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Masked gradient-based causal structure learning
Ignavier Ng, Zhuangyan Fang, Shengyu Zhu, and Zhitang Chen · 2019
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A graph autoencoder approach to causal structure learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen, and Zhuangyan Fang · 2019
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Causality for machine learning
Bernhard Schölkopf · 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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Causal discovery with reinforcement learning
Shengyu Zhu and Zhitang Chen · 2019
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Causal discovery with reinforcement learning
Shengyu Zhu, Ignavier Ng, and Zhitang Chen · 2020
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