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Independent component analysis provides a principled framework for unsupervised representation learning, with solid theory on the identifiability of the latent code that generated the data, given only observations of mixtures thereof.
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Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Blind source separation of natural signals based on approximate complexity minimization
Petteri Pajunen · 1999
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Source separation in post-nonlinear mixtures
Anisse Taleb and Christian Jutten · 1999
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The three easy routes to independent component analysis; contrasts and geometry
Jean-François Cardoso · 2001
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Independent Component Analysis
Aapo Hyvärinen, Juha Karhunen, and Erkki Oja · 2001
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Beyond correlational analysis: Recent innovations in theory and method
James Mahoney · 2001
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Blind separation of instantaneous mixtures of nonstationary sources
Dinh-Tuan Pham and J-F Cardoso · 2001
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Historical analysis of political processes
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Causal models as minimal descriptions of multivariate systems, 2006
Jan Lemeire and Erik Dirkx · 2006
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A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
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Methods of information geometry , volume 191
Shun-ichi Amari and Hiroshi Nagaoka · 2007
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Liouville’s theorem revisited
Ruy Tojeiro · 2007
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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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Minimal nonlinear distortion principle for nonlinear independent component analysis
Kun Zhang and Laiwan Chan · 2008
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Causality
Judea Pearl · 2009
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvärinen · 2009
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Inferring deterministic causal relations
Povilas Daniušis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, and Bernhard Schölkopf · 2010
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Causal inference using the algorithmic Markov condition
Dominik Janzing and Bernhard Schölkopf · 2010
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Telling cause from effect based on high-dimensional observations
Dominik Janzing, Patrik O Hoyer, and Bernhard Schölkopf · 2010
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Inverse dynamics control of floating base systems using orthogonal decomposition
Michael Mistry, Jonas Buchli, and Stefan Schaal · 2010
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Causal Markov condition for submodular information measures
B. Steudel, D. Janzing, and B. Schölkopf · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Conformal maps and the theorem of Liouville
Mirjam Soeten · 2011
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Testing whether linear equations are causal: A free probability theory approach
Jakob Zscheischler, Dominik Janzing, and Kun Zhang · 2011
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Information-geometric approach to inferring causal directions
Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniušis, Bastian Steudel, and Bernhard Schölkopf · 2012
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On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, and J Mooij · 2012
Learning independent causal mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, and Bernhard Schölkopf · 2018
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A spectral regularizer for unsupervised disentanglement
Aditya Ramesh, Youngduck Choi, and Yann LeCun · 2018
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Short notes on divergence measures, 2018
Danilo Jimenez Rezende · 2018
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Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Residual flows for invertible generative modeling
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Replacing causal faithfulness with algorithmic independence of conditionals
Jan Lemeire and Dominik Janzing · 2013
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Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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External validity: From do-calculus to transportability across populations
Judea Pearl and Elias Bareinboim · 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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Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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The Incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
Luigi Gresele, Paul K Rubenstein, Arash Mehrjou, Francesco Locatello, and Bernhard Schölkopf · 2019
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Aapo Hyvärinen, Hiroaki Sasaki, and Richard Turner · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria · 2019
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Semi-generative modelling: Covariate-shift adaptation with cause and effect features
Julius von Kügelgen, Alexander Mey, and Marco Loog · 2019
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Near-optimal reinforcement learning in dynamic treatment regimes
Junzhe Zhang and Elias Bareinboim · 2019
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Counterfactuals uncover the modular structure of deep generative models
M Besserve, A Mehrjou, R Sun, and B Schölkopf · 2020
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Robust learning with the hilbert-schmidt independence criterion
Daniel Greenfeld and Uri Shalit · 2020
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Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, and Aapo Hyvarinen · 2020
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Hidden Markov nonlinear ICA: Unsupervised learning from nonstationary time series
Hermanni Hälvä and Aapo Hyvärinen · 2020
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Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
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Variational autoencoders and nonlinear ICA: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvärinen · 2020
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Structural autoencoders improve representations for generation and transfer
Felix Leeb, Yashas Annadani, Stefan Bauer, and Bernhard Schölkopf · 2020
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Sample-efficient reinforcement learning via counterfactual-based data augmentation
Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, and Bernhard Schölkopf · 2020
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Causal discovery with general non-linear relationships using non-linear ICA
Ricardo Pio Monti, Kun Zhang, and Aapo Hyvärinen · 2020
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The Hessian penalty: A weak prior for unsupervised disentanglement
William S. Peebles, John Peebles, Jun-Yan Zhu, Alexei A. Efros, and Antonio Torralba · 2020
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Scientific explanation and the causal structure of the world
Wesley C Salmon · 2020
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Disentangled generative causal representation learning
Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, and Tong Zhang · 2020
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Scipy 1.0: fundamental algorithms for scientific computing in python
Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2020
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Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, and Bernhard Schölkopf · 2020
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Information geometry
Shun-ichi Amari · 2021
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Distrax: Probability distributions in JAX, 2021
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Recurrent independent mechanisms
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Conditional variance penalties and domain shift robustness
Christina Heinze-Deml and Nicolai Meinshausen · 2021
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Causal version of principle of insufficient reason and maxent
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Causal autoregressive flows
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Normalizing flows for probabilistic modeling and inference
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Anchor regression: Heterogeneous data meet causality
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Toward causal representation learning
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Self-supervised learning with data augmentations provably isolates content from style
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