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Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof.
Analyse des liaisons de probabilité
G. Darmois · 1951
Earlier work this paper cites.
Independent component analysis, a new concept?
P. Comon · 1994
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
A. Hyvärinen and P. Pajunen · 1999
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Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces
A. Hyvärinen and P. Hoyer · 2000
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Causation, prediction, and search
P. Spirtes, C. N. Glymour, and R. Scheines · 2000
Earlier work this paper cites.
Topographic independent component analysis
A. Hyvärinen, P. O. Hoyer, and M. Inki · 2001
Earlier work this paper cites.
All of statistics: a concise course in statistical inference , volume 26
L. Wasserman · 2004
Earlier work this paper cites.
Structure by architecture: Structured representations without regularization
F. Leeb, G. Lanzillotta, Y. Annadani, M. Besserve, S. Bauer, and B. Schölkopf · 2006
Earlier work this paper cites.
Theory of point estimation
E. L. Lehmann and G. Casella · 2006
Earlier work this paper cites.
Learning the structure of linear latent variable models
R. Silva, R. Scheines, C. Glymour, P. Spirtes, and D. M. Chickering · 2006
Earlier work this paper cites.
Causality
J. Pearl · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
K. Zhang and A. Hyvärinen · 2009
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
A. Hyvärinen and H. Morioka · 2016
Earlier work this paper cites.
Nonlinear ICA of temporally dependent stationary sources
A. Hyvärinen and H. Morioka · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
Earlier work this paper cites.
Triad constraints for learning causal structure of latent variables
R. Cai, F. Xie, C. Glymour, Z. Hao, and K. Zhang · 2019
Earlier work this paper cites.
Neural spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
Earlier work this paper cites.
The Incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf · 2019
Earlier work this paper cites.
Nonlinear ICA using auxiliary variables and generalized contrastive learning
A. Hyvärinen, H. Sasaki, and R. Turner · 2019
Earlier work this paper cites.
Relative gradient optimization of the Jacobian term in unsupervised deep learning
L. Gresele, G. Fissore, A. Javaloy, B. Schölkopf, and A. Hyvärinen · 2020
Earlier work this paper cites.
Hidden markov nonlinear ICA: Unsupervised learning from nonstationary time series
H. Hälvä and A. Hyvärinen · 2020
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Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen · 2020
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Causal discovery with general non-linear relationships using non-linear ICA
R. P. Monti, K. Zhang, and A. Hyvärinen · 2020
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Generalized independent noise condition for estimating latent variable causal graphs
F. Xie, R. Cai, B. Huang, C. Glymour, Z. Hao, and K. Zhang · 2020
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L. Gresele, J. von Kügelgen, V. Stimper, B. Schölkopf, and M. Besserve · 2021
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Disentangling identifiable features from noisy data with structured nonlinear ICA
Partial disentanglement via mechanism sparsity
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
S. Lachapelle, P. Rodriguez, Y. Sharma, K. E. Everett, R. Le Priol, A. Lacoste, and S. Lacoste-Julien · 2022
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Citris: Causal identifiability from temporal intervened sequences
P. Lippe, S. Magliacane, S. Löwe, Y. M. Asano, T. Cohen, and S. Gavves · 2022
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Weight-variant latent causal models
Y. Liu, Z. Zhang, D. Gong, M. Gong, B. Huang, A. van den Hengel, K. Zhang, and J. Qinfeng Shi · 2022
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Understanding latent correlation-based multiview learning and self-supervision: An identifiability perspective
Q. Lyu, X. Fu, W. Wang, and S. Lu · 2022
Later among the works it cites.
Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis
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Identification of linear non-gaussian latent hierarchical structure
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Learning temporally causal latent processes from general temporal data
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