Patrik O. Hoyer

J. Mach. Learn. Res., 2013

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Data-driven covariate selection for nonparametric estimation of causal effects.

[BibT_eX]

[DOI]

Peter Spirtes

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Learning linear cyclic causal models with latent variables.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2012

Statistical test for consistent estimation of causal effects in linear non-Gaussian models.

[BibT_eX]

[DOI]

Peter Spirtes

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Estimating a Causal Order among Groups of Variables in Linear Models.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

2011

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2011

Causal Search in Structural Vector Autoregressive Models.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems (NIPS) Mini-Symposium on Causality in Time Series, 2011

Noisy-OR Models with Latent Confounding.

[BibT_eX]

[DOI]

Proceedings of the UAI 2011, 2011

2010

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

Combining Experiments to Discover Linear Cyclic Models with Latent Variables.

[BibT_eX]

[DOI]

Richard Scheines

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Artificial Intelligence, 2010

Telling cause from effect based on high-dimensional observations.

[BibT_eX]

[DOI]

Dominik Janzing

Bernhard Schölkopf

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Natural Image Statistics - A Probabilistic Approach to Early Computational Vision

[BibT_eX]

[DOI]

Jarmo Hurri

Computational Imaging and Vision 39, Springer, ISBN: 978-1-84882-491-1, 2009

Estimation of linear non-Gaussian acyclic models for latent factors.

[BibT_eX]

[DOI]

Shohei Shimizu

Neurocomputing, 2009

Bayesian Discovery of Linear Acyclic Causal Models.

[BibT_eX]

[DOI]

Proceedings of the UAI 2009, 2009

2008

Estimation of causal effects using linear non-Gaussian causal models with hidden variables.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2008

Discovering Cyclic Causal Models by Independent Components Analysis.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

Causal discovery of linear acyclic models with arbitrary distributions.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

Nonlinear causal discovery with additive noise models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity.

[BibT_eX]

[DOI]

Shohei Shimizu

Proceedings of the Machine Learning, 2008

2007

Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks.

[BibT_eX]

[DOI]

Maria Asunción Vicente

IEEE Trans. Pattern Anal. Mach. Intell., 2007

2006

A Linear Non-Gaussian Acyclic Model for Causal Discovery.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2006

Finding a causal ordering via independent component analysis.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2006

Estimation of linear, non-gaussian causal models in the presence of confounding latent variables.

[BibT_eX]

[DOI]

Shohei Shimizu

Antti J. Kerminen

Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Testing Significance of Mixing and Demixing Coefficients in ICA.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

New Permutation Algorithms for Causal Discovery Using ICA.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

2005

Discovery of Non-gaussian Linear Causal Models using ICA.

[BibT_eX]

[DOI]

Proceedings of the UAI '05, 2005

2004

Non-negative Matrix Factorization with Sparseness Constraints.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2004

2003

Modeling receptive fields with non-negative sparse coding.

[BibT_eX]

[DOI]

Neurocomputing, 2003

2002

Probabilistic models of early vision.

[BibT_eX]

[DOI]

PhD thesis, 2002

Sparse coding of natural contours.

[BibT_eX]

[DOI]

Neurocomputing, 2002

Non-negative sparse coding.

[BibT_eX]

[DOI]

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2002

Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Topographic Independent Component Analysis.

[BibT_eX]

[DOI]

Mika Inki

Neural Comput., 2001

Topographic independent component analysis as a model of V1 organization and receptive fields.

[BibT_eX]

[DOI]

Neurocomputing, 2001

2000

A new approach to uncover dynamic phase coordination and synchronization.

[BibT_eX]

[DOI]

Dirk Hoyer

Ulrich Zwiener

IEEE Trans. Biomed. Eng., 2000

Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces.

[BibT_eX]

[DOI]

Neural Comput., 2000

Topographic ICA as a Model of V1 Receptive Fields.

[BibT_eX]

[DOI]

Mika Inki

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Feature Extraction from Color and Stereo Images Using ICA.

[BibT_eX]

[DOI]

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Topographic ICA as a Model of Natural Image Statistics.

[BibT_eX]

[DOI]

Mika Inki

Proceedings of the Biologically Motivated Computer Vision, 2000

1999

Image Feature Extraction and Denoising by Sparse Coding.

[BibT_eX]

[DOI]

Erkki Oja

Pattern Anal. Appl., 1999

Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Independent subspace analysis shows emergence of phase and shift invariant features from natural images.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference Neural Networks, 1999

Estimating signal-adapted wavelets using sparseness criteria.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference Neural Networks, 1999

1998

Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation.

[BibT_eX]

[DOI]