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2010 – 2019
- 2013
[j17]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Experiment selection for causal discovery. J. Mach. Learn. Res. 14(1): 3041-3071 (2013)- [c29]Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Data-driven covariate selection for nonparametric estimation of causal effects. AISTATS 2013: 256-264 - [c28]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI 2013 - [i11]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR abs/1309.6836 (2013) - 2012
- [j16]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res. 13: 3387-3439 (2012) - [c27]Doris Entner, Patrik O. Hoyer:
Estimating a Causal Order among Groups of Variables in Linear Models. ICANN (2) 2012: 84-91 - [c26]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 - [c25]Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Statistical test for consistent estimation of causal effects in linear non-Gaussian models. AISTATS 2012: 364-372 - [i10]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. CoRR abs/1202.3735 (2012) - [i9]Patrik O. Hoyer, Antti Hyttinen:
Bayesian Discovery of Linear Acyclic Causal Models. CoRR abs/1205.2641 (2012) - [i8]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. CoRR abs/1206.3260 (2012) - [i7]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. CoRR abs/1206.3273 (2012) - [i6]Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer:
Discovery of non-gaussian linear causal models using ICA. CoRR abs/1207.1413 (2012) - [i5]Doris Entner, Patrik O. Hoyer:
Estimating a Causal Order among Groups of Variables in Linear Models. CoRR abs/1207.1977 (2012) - [i4]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) - 2011
- [j15]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. J. Mach. Learn. Res. 12: 1225-1248 (2011) - [c24]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. UAI 2011: 363-372 - [c23]Alessio Moneta, Nadine Chlass, Doris Entner, Patrik O. Hoyer:
Causal Search in Structural Vector Autoregressive Models. NIPS Mini-Symposium on Causality in Time Series 2011: 95-114 - 2010
- [j14]Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer:
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. 11: 1709-1731 (2010) - [c22]Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf:
Telling cause from effect based on high-dimensional observations. ICML 2010: 479-486 - [c21]Doris Entner, Patrik O. Hoyer:
Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models. JSAI-isAI Workshops 2010: 181-195 - [c20]Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines:
Combining Experiments to Discover Linear Cyclic Models with Latent Variables. AISTATS 2010: 185-192
2000 – 2009
- 2009
- [b2]Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer:
Natural Image Statistics - A Probabilistic Approach to Early Computational Vision. Computational Imaging and Vision 39, Springer 2009, ISBN 978-1-84882-490-4, pp. 1-451 - [j13]Shohei Shimizu
, Patrik O. Hoyer, Aapo Hyvärinen:
Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing 72(7-9): 2024-2027 (2009) - [c19]Patrik O. Hoyer, Antti Hyttinen:
Bayesian Discovery of Linear Acyclic Causal Models. UAI 2009: 240-248 - 2008
- [j12]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. Reason. 49(2): 362-378 (2008) - [c18]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 - [c17]Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf:
Nonlinear causal discovery with additive noise models. NIPS 2008: 689-696 - [c16]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. UAI 2008: 282-289 - [c15]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. UAI 2008: 366-374 - 2007
- [j11]Maria Asunción 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) - 2006
- [j10]Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano:
Finding a causal ordering via independent component analysis. Comput. Stat. Data Anal. 50(11): 3278-3293 (2006) - [j9]Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen:
A Linear Non-Gaussian Acyclic Model for Causal Discovery. J. Mach. Learn. Res. 7: 2003-2030 (2006) - [c14]Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen:
New Permutation Algorithms for Causal Discovery Using ICA. ICA 2006: 115-122 - [c13]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 - [c12]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 - [i3]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) - 2005
- [c11]Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer:
Discovery of Non-gaussian Linear Causal Models using ICA. UAI 2005: 525-533 - 2004
- [j8]Patrik O. Hoyer:
Non-negative Matrix Factorization with Sparseness Constraints. J. Mach. Learn. Res. 5: 1457-1469 (2004) - [i2]Patrik O. Hoyer:
Non-negative matrix factorization with sparseness constraints. CoRR cs.LG/0408058 (2004) - 2003
- [j7]Patrik O. Hoyer:
Modeling receptive fields with non-negative sparse coding. Neurocomputing 52-54: 547-552 (2003) - 2002
- [b1]Patrik O. Hoyer:
Probabilistic models of early vision. Aalto University, Helsinki, Finland, 2002 - [j6]Patrik O. Hoyer, Aapo Hyvärinen:
Sparse coding of natural contours. Neurocomputing 44-46: 459-466 (2002) - [c10]Patrik O. Hoyer, Aapo Hyvärinen:
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. NIPS 2002: 277-284 - [c9]Patrik O. Hoyer:
Non-negative sparse coding. NNSP 2002: 557-565 - [i1]Patrik O. Hoyer:
Non-negative sparse coding. CoRR cs.NE/0202009 (2002) - 2001
- [j5]Aapo Hyvärinen, Patrik O. Hoyer:
Topographic independent component analysis as a model of V1 organization and receptive fields. Neurocomputing 38-40: 1307-1315 (2001) - [j4]Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki:
Topographic Independent Component Analysis. Neural Comput. 13(7): 1527-1558 (2001) - 2000
- [j3]Aapo Hyvärinen, Patrik O. Hoyer:
Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces. Neural Comput. 12(7): 1705-1720 (2000) - [j2]Dirk Hoyer, Patrik O. Hoyer, Ulrich Zwiener:
A new approach to uncover dynamic phase coordination and synchronization. IEEE Trans. Biomed. Eng. 47(1): 68-74 (2000) - [c8]Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki:
Topographic ICA as a Model of Natural Image Statistics. Biologically Motivated Computer Vision 2000: 535-544 - [c7]Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki:
Topographic ICA as a Model of V1 Receptive Fields. IJCNN (4) 2000: 83-88 - [c6]Patrik O. Hoyer, Aapo Hyvärinen:
Feature Extraction from Color and Stereo Images Using ICA. IJCNN (3) 2000: 369-376
1990 – 1999
- 1999
- [j1]Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer:
Image Feature Extraction and Denoising by Sparse Coding. Pattern Anal. Appl. 2(2): 104-110 (1999) - [c5]Aapo Hyvärinen, Patrik O. Hoyer:
Independent subspace analysis shows emergence of phase and shift invariant features from natural images. IJCNN 1999: 1059-1064 - [c4]Patrik O. Hoyer, Aapo Hyvärinen:
Estimating signal-adapted wavelets using sparseness criteria. IJCNN 1999: 2570-2575 - [c3]Aapo Hyvärinen, Patrik O. Hoyer:
Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. NIPS 1999: 827-833 - 1998
- [c2]Aapo Hyvärinen, Erkki Oja, Patrik O. Hoyer, Jarmo Hurri:
Image feature extraction by sparse coding and independent component analysis. ICPR 1998: 1268-1273 - [c1]Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja:
Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. NIPS 1998: 473-479
Coauthor Index
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