Patrik O. Hoyer

Doris Entner, Patrik O. Hoyer, Peter Spirtes: Statistical test for consistent estimation of causal effects in linear non-Gaussian models. Journal of Machine Learning Research - Proceedings Track 22: 364-372 (2012)

Doris Entner, Patrik O. Hoyer: Estimating a Causal Order among Groups of Variables in Linear Models. ICANN (2) 2012: 84-91

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer: Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. UAI 2012: 387-396

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer: Noisy-OR Models with Latent Confounding. CoRR abs/1202.3735 (2012)

Patrik O. Hoyer, Antti Hyttinen: Bayesian Discovery of Linear Acyclic Causal Models. CoRR abs/1205.2641 (2012)

Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu: Causal discovery of linear acyclic models with arbitrary distributions. CoRR abs/1206.3260 (2012)

Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer: Discovering Cyclic Causal Models by Independent Components Analysis. CoRR abs/1206.3273 (2012)

Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer: Discovery of non-gaussian linear causal models using ICA. CoRR abs/1207.1413 (2012)

Doris Entner, Patrik O. Hoyer: Estimating a Causal Order among Groups of Variables in Linear Models. CoRR abs/1207.1977 (2012)

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer: Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. CoRR abs/1210.4879 (2012)

Alessio Moneta, Nadine Chlass, Doris Entner, Patrik O. Hoyer: Causal Search in Structural Vector Autoregressive Models. Journal of Machine Learning Research - Proceedings Track 12: 95-114 (2011)

Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen: DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. Journal of Machine Learning Research 12: 1225-1248 (2011)

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer: Noisy-OR Models with Latent Confounding. UAI 2011: 363-372

Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines: Combining Experiments to Discover Linear Cyclic Models with Latent Variables. Journal of Machine Learning Research - Proceedings Track 9: 185-192 (2010)

Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer: Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. Journal of Machine Learning Research 11: 1709-1731 (2010)

Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf: Telling cause from effect based on high-dimensional observations. ICML 2010: 479-486

Doris Entner, Patrik O. Hoyer: Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models. JSAI-isAI Workshops 2010: 181-195

Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen: Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing 72(7-9): 2024-2027 (2009)

Patrik O. Hoyer, Antti Hyttinen: Bayesian Discovery of Linear Acyclic Causal Models. UAI 2009: 240-248

Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, Markus Palviainen: Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reasoning 49(2): 362-378 (2008)

Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer: Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. ICML 2008: 424-431

Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf: Nonlinear causal discovery with additive noise models. NIPS 2008: 689-696

Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu: Causal discovery of linear acyclic models with arbitrary distributions. UAI 2008: 282-289

Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer: Discovering Cyclic Causal Models by Independent Components Analysis. UAI 2008: 366-374

M. Asuncion Vicente, Patrik O. Hoyer, Aapo Hyvärinen: Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 29(5): 896-900 (2007)

Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano: Finding a causal ordering via independent component analysis. Computational Statistics & Data Analysis 50(11): 3278-3293 (2006)

Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen: A Linear Non-Gaussian Acyclic Model for Causal Discovery. Journal of Machine Learning Research 7: 2003-2030 (2006)

Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen: New Permutation Algorithms for Causal Discovery Using ICA. ICA 2006: 115-122

Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen: Testing Significance of Mixing and Demixing Coefficients in ICA. ICA 2006: 901-908

Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen: Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. Probabilistic Graphical Models 2006: 155-162

Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen: Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. CoRR abs/cs/0603038 (2006)

Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer: Discovery of Non-gaussian Linear Causal Models using ICA. UAI 2005: 525-533

Patrik O. Hoyer: Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 5: 1457-1469 (2004)

Patrik O. Hoyer: Non-negative matrix factorization with sparseness constraints. CoRR cs.LG/0408058 (2004)

Patrik O. Hoyer: Modeling receptive fields with non-negative sparse coding. Neurocomputing 52-54: 547-552 (2003)

Patrik O. Hoyer, Aapo Hyvärinen: Sparse coding of natural contours. Neurocomputing 44-46: 459-466 (2002)

Patrik O. Hoyer, Aapo Hyvärinen: Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. NIPS 2002: 277-284

Patrik O. Hoyer: Non-negative sparse coding. CoRR cs.NE/0202009 (2002)

Aapo Hyvärinen, Patrik O. Hoyer: Topographic independent component analysis as a model of V1 organization and receptive fields. Neurocomputing 38-40: 1307-1315 (2001)

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki: Topographic Independent Component Analysis. Neural Computation 13(7): 1527-1558 (2001)

Aapo Hyvärinen, Patrik O. Hoyer: Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces. Neural Computation 12(7): 1705-1720 (2000)

Dirk Hoyer, Patrik O. Hoyer, Ulrich Zwiener: A new approach to uncover dynamic phase coordination and synchronization. IEEE Trans. Biomed. Engineering 47(1): 68-74 (2000)

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki: Topographic ICA as a Model of Natural Image Statistics. Biologically Motivated Computer Vision 2000: 535-544

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki: Topographic ICA as a Model of V1 Receptive Fields. IJCNN (4) 2000: 83-88

Patrik O. Hoyer, Aapo Hyvärinen: Feature Extraction from Color and Stereo Images Using ICA. IJCNN (3) 2000: 369-376

Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer: Image Feature Extraction and Denoising by Sparse Coding. Pattern Anal. Appl. 2(2): 104-110 (1999)

Aapo Hyvärinen, Patrik O. Hoyer: Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. NIPS 1999: 827-833

Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja: Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. NIPS 1998: 473-479