P. Gao, A. Honkela, M. Rattray, N. D. Lawrence. (2008). Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. Bioinformatics 24(16), pp. i70-i75. Appeared in Proceedings of ECCB 2008.
doi:10.1093/bioinformatics/btn278
A. Honkela, M. Tornio, T. Raiko, J. Karhunen. (2008). Natural Conjugate Gradient in Variational Inference. In Proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007), Kitakyushu, Japan, pp. 305-314.
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A. Honkela, J. Seppä, E. Alhoniemi. (2008). Agglomerative Independent Variable Group Analysis. Neurocomputing 71(7--9), pp. 1311-1320.
doi:10.1016/j.neucom.2007.11.024
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E. Alhoniemi, A. Honkela, K. Lagus, J. Seppä, P. Wagner, H. Valpola. (2007). Compact Modeling of Data Using Independent Variable Group Analysis. IEEE Transactions on Neural Networks 18(6), pp. 1762-1776.
doi:10.1109/TNN.2007.900809
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M. Harva, A. Kabán. (2007). Variational Learning for Rectified Factor Analysis. Signal Processing 87(3), pp. 509-527.
doi:10.1016/j.sigpro.2006.06.006
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M. Harva. (2007). A Variational EM Approach to Predictive Uncertainty. Neural Networks 20(4), pp. 550-558.
doi:10.1016/j.neunet.2007.04.010
A. Honkela, J. Seppä, E. Alhoniemi. (2007). Agglomerative Independent Variable Group Analysis. In Proc. 15th European Symposium on Artificial Neural Networks (ESANN 2007), Bruges, Belgium, pp. 55-60.
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A. Honkela, H. Valpola, A. Ilin, J. Karhunen. (2007). Blind Separation of Nonlinear Mixtures by Variational Bayesian Learning. Digital Signal Processing 17(5), pp. 914-934.
doi:10.1016/j.dsp.2007.02.009
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T. Raiko, H. Valpola, M. Harva, J. Karhunen. (2007). Building blocks for variational Bayesian learning of latent variable models. Journal of Machine Learning Research 8(Jan), pp. 155-201.
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T. Raiko, A. Ilin, J. Karhunen. (2007). Principal Component Analysis for Large Scale Problems with Lots of Missing Values. In Proceedings of the 18th European Conference on Machine Learning (ECML 2007), Warsaw, Poland.
T. Raiko, A. Ilin, J. Karhunen. (2007). Principal Component Analysis for Sparse High-Dimensional Data. In Proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007), Kitakyushu, Japan.
M. Tornio, A. Honkela, J. Karhunen. (2007). Time Series Prediction with Variational Bayesian Nonlinear State-Space Models. In Proceedings of the European Symposium on Time Series Prediction (ESTSP 2007), Espoo, Finland, pp. 11-19.
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E. Alhoniemi, A. Honkela, K. Lagus, J. Seppä, P. Wagner, H. Valpola. (2006). Compact Modeling of Data Using Independent Variable Group Analysis. Technical report E3, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland. Available at http://www.cis.hut.fi/Publications/.
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M. Harva, S. Raychaudhury. (2006). Bayesian Estimation of Time Delays Between Unevenly Sampled Signals. In Proc. Int. Workshop on Machine Learning for Signal Processing (MLSP'06), Maynooth, Ireland, pp. 111-116.
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M. Harva. (2006). A Variational EM Approach to Predicting Uncertainty in Supervised Learning. In Proc. World Congress on Computational Intelligence (WCCI'06), Vancouver, BC, Canada, pp. 11091-11095.
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A. Honkela. (2006). Distributed Bayes Blocks for Variational Bayesian Learning. In Conference on High Performance Computing for Statistical Inference, Dublin, Ireland.
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A. Honkela, M. Harva, T. Raiko, H. Valpola, J. Karhunen. (2006). Bayes Blocks: A Python Toolbox for Variational Bayesian Learning. In NIPS*2006 Workshop on Machine Learning Open Source Software, Whistler, B.C., Canada.
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A. Honkela, M. Tornio, T. Raiko. (2006). Variational Bayes for Continuous-Time Nonlinear State-Space Models. In NIPS*2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler, B.C., Canada.
Workshop homepage
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A. Ilin. (2006). Advanced Source Separation Methods with Applications to Spatio-Temporal Datasets. PhD thesis, Helsinki University of Technology, Espoo, Finland.
Electronic dissertation
K. Kersting, L. D. Raedt, T. Raiko. (2006). Logical Hidden Markov Models. Journal of Artificial Intelligence Research 25(), pp. 425-456.
Publisher electronic edition
J. Nikkilä, A. Honkela, S. Kaski. (2006). Exploring the Independence of Gene Regulatory Modules. In J. Rousu, S. Kaski, E. Ukkonen, editors, Proc. Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, pp. 131-136.
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L. Nolan, M. Harva, A. Kabán, S. Raychaudhury. (2006). A data-driven Bayesian approach for finding young stellar populations in early-type galaxies from their UV-optical spectra. Monthly Notices of the Royal Astronomical Society 366(1), pp. 321-338.
doi:10.1111/j.1365-2966.2005.09868.x
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T. Raiko. (2006). Bayesian Inference in Nonlinear and Relational Latent Variable Models. PhD thesis, Helsinki University of Technology, Espoo, Finland.
Electronic dissertation
T. Raiko, H. Valpola, M. Harva, J. Karhunen. (2006). Building blocks for variational Bayesian learning of latent variable models. Technical report E4, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland. Available at http://www.cis.hut.fi/Publications/. Accepted for publication in Journal of Machine Learning Research conditioned on minor revisions..
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T. Raiko, M. Tornio, A. Honkela, J. Karhunen. (2006). State Inference in Variational Bayesian Nonlinear State-Space Models. In Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), Charleston, South Carolina, USA, pp. 222-229.
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T. Raiko. (2006). Higher Order Statistics in Play-out Analysis. In T. Honkela, T. Raiko, J. Kortela, H. Valpola, editors, Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), Espoo, Finland.
M. Tornio, T. Raiko. (2006). Variational Bayesian Approach for Nonlinear Identification and Control. In Proceedings of the IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems, NMPC FS06, Grenoble, France, pp. 41-46.
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H. Valpola, A. Honkela. (2006). Hyperparameter Adaptation in Variational Bayes for the Gamma Distribution. Technical report E6, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland. Available at http://www.cis.hut.fi/Publications/.
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M. Harva, A. Kabán. (2005). A Variational Bayesian Method for Rectified Factor Analysis. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'05), Montreal, Canada, pp. 185-190.
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M. Harva, T. Raiko, A. Honkela, H. Valpola, J. Karhunen. (2005). Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. In Proc. of the 21st Conf. on Uncertainty in Artificial Intelligence (UAI 2005), Edinburgh, Scotland, pp. 259-266.
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A. Honkela, H. Valpola. (2005). Unsupervised Variational Bayesian Learning of Nonlinear Models. In L. Saul, Y. Weiss, L. Bottou, editors, Advances in Neural Information Processing Systems 17, pp. 593-600, MIT Press.
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A. Honkela, T. Östman, R. Vigário. (2005). Empirical evidence of the linear nature of magnetoencephalograms. In Proc. 13th European Symposium on Artificial Neural Networks (ESANN 2005), Bruges, Belgium, pp. 285-290.
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A. Honkela. (2005). Advances in Variational Bayesian Nonlinear Blind Source Separation. PhD thesis, Helsinki University of Technology, Espoo, Finland.
Electronic dissertation
A. Ilin, H. Valpola. (2005). On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models. Neural Processing Letters 22(2), pp. 183-204.
doi:10.1007/s11063-005-5265-0
K. Kersting, T. Raiko. (2005). 'Say EM' for Selecting Probabilistic Models for Logical Sequences. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, Edinburgh, Scotland, pp. 300-307.
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K. Lagus, E. Alhoniemi, J. Seppä, A. Honkela, P. Wagner. (2005). Independent Variable Group Analysis in Learning Compact Representations for Data. In T. Honkela, V. Könönen, M. Pöllä, O. Simula, editors, Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05), Espoo, Finland, pp. 49-56.
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T. Raiko, M. Tornio. (2005). Learning Nonlinear State-Space Models for Control. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'05), Montreal, Canada, pp. 815-820.
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T. Raiko. (2005). Nonlinear Relational Markov Networks with an Application to the Game of Go. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 2005), Warsaw, Poland, pp. 989-996.
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M. Harva. (2004). Hierarchical Variance Models of Image Sequences. Master's thesis, Helsinki University of Technology, Espoo.
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A. Honkela. (2004). Approximating Nonlinear Transformations of Probability Distributions for Nonlinear Independent Component Analysis. In Proc. 2004 IEEE Int. Joint Conf. on Neural Networks (IJCNN 2004), Budapest, Hungary, pp. 2169-2174.
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A. Honkela, H. Valpola. (2004). Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. IEEE Transactions on Neural Networks 15(4), pp. 800-810.
doi:10.1109/TNN.2004.828762
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A. Honkela, S. Harmeling, L. Lundqvist, H. Valpola. (2004). Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. In C. G. Puntonet, A. Prieto, editors, Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2004), Granada, Spain, pp. 790-797.
Publisher electronic edition
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A. Ilin, A. Honkela. (2004). Postnonlinear Independent Component Analysis by Variational Bayesian Learning. In C. G. Puntonet, A. Prieto, editors, Proc. of the Fifth Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2004), Granada, Spain, pp. 766-773.
Publisher electronic edition
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A. Ilin, H. Valpola, E. Oja. (2004). Nonlinear Dynamical Factor Analysis for State Change Detection. IEEE Transactions on Neural Networks 15(3), pp. 559-575.
doi:10.1109/TNN.2004.826129
A. Ilin, S. Achard, C. Jutten. (2004). Bayesian versus Constrained Structure Approaches for Source Separation in Post-Nonlinear Mixtures. In Proc. 2004 IEEE Int. Joint Conf. on Neural Networks (IJCNN 2004), Budapest, Hungary, pp. 2181-2186.
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C. Jutten, J. Karhunen. (2004). Advances in Blind Source Separation (BSS) and Independent Component Analysis (ICA) for nonlinear mixtures. International Journal of Neural Systems 14(5), pp. 267-292.
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T. Raiko. (2004). Partially observed values. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'04), Budapest, Hungary, pp. 2825-2830.
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H. Valpola, M. Harva, J. Karhunen. (2004). Hierarchical Models of Variance Sources. Signal Processing 84(2), pp. 267-282.
doi:10.1016/j.sigpro.2003.10.014
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A. Honkela, H. Valpola. (2003). On-line Variational Bayesian Learning. In Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, pp. 803-808.
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A. Honkela, H. Valpola, J. Karhunen. (2003). Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches. Neural Processing Letters 17(2), pp. 191-203.
doi:10.1023/A:1023655202546
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A. Ilin, H. Valpola. (2003). On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models. In Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, pp. 915-920.
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C. Jutten, J. Karhunen. (2003). Advances in nonlinear blind source separation. In Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), pp. 245-256. Invited paper in the special session on nonlinear ICA and BSS.
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T. Raiko, H. Valpola, T. Östman, J. Karhunen. (2003). Missing values in hierarchical nonlinear factor analysis. In Proc. of the Int. Conf. on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP 2003), Istanbul, Turkey, pp. 185-189.
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H. Valpola, M. Harva, J. Karhunen. (2003). Hierarchical Models of Variance Sources. In Proc. 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, pp. 83-88.
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H. Valpola, T. Östman, J. Karhunen. (2003). Nonlinear Independent Factor Analysis by Hierarchical Models. In Proc. 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, pp. 257-262.
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H. Valpola, E. Oja, A. Ilin, A. Honkela, J. Karhunen. (2003). Nonlinear Blind Source Separation by Variational Bayesian Learning. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E86-A(3), pp. 532-541.
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A. Honkela. (2002). Speeding Up Cyclic Update Schemes by Pattern Searches. In Proc. of the 9th Int. Conf. on Neural Information Processing (ICONIP'02), Singapore, pp. 512-516.
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H. Valpola, A. Honkela, J. Karhunen. (2002). An Ensemble Learning Approach to Nonlinear Dynamic Blind Source Separation Using State-Space Models. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'02), Honolulu, Hawaii, USA, pp. 460-465.
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H. Valpola, J. Karhunen. (2002). An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models. Neural Computation 14(11), pp. 2647-2692.
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A. Honkela. (2001). Nonlinear Switching State-Space Models. Master's thesis, Helsinki University of Technology, Espoo.
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A. Honkela, J. Karhunen. (2001). An Ensemble Learning Approach to Nonlinear Independent Component Analysis. In Proc. European Conf. on Circuit Theory and Design (ECCTD'01), Espoo, Finland, pp. I-41-44.
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A. Hyvärinen, J. Karhunen, E. Oja. (2001). Independent Component Analysis, J. Wiley.
A. Iline, H. Valpola, E. Oja. (2001). Detecting Process State Changes by Nonlinear Blind Source Separation. In Proc. Int. Conf. on Independent Component Analysis and Signal Separation (ICA2001), San Diego, USA, pp. 704-709.
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K. Lagus, E. Alhoniemi, H. Valpola. (2001). Independent Variable Group Analysis. In G. Dorffner, H. Bischof, K. Hornik, editors, Proc. of ICANN2001: Lecture Notes in Computer Science 2130, pp. 203-210, Springer-Verlag.
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T. Raiko. (2001). Hierarchical Nonlinear Factor Analysis. Master's thesis, Helsinki University of Technology, Espoo, Finland.
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T. Raiko, H. Valpola. (2001). Missing values in nonlinear factor analysis. In Proc. of the 8th Int. Conf. on Neural Information Processing (ICONIP'01), Shanghai, pp. 822-827.
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J. Särelä, H. Valpola, R. Vigário, E. Oja. (2001). Dynamical Factor Analysis of Rhythmic Magnetoencephalographic Activity. In Proc. Int. Conf. on Independent Component Analysis and Signal Separation (ICA2001), San Diego, USA, pp. 451-456.
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H. Valpola, T. Raiko, J. Karhunen. (2001). Building Blocks for Hierarchical Latent Variable Models. In Proc. 3rd Int. Conf. on Independent Component Analysis and Signal Separation (ICA2001), San Diego, USA, pp. 710-715.
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H. Valpola, A. Honkela, J. Karhunen. (2001). Nonlinear Static and Dynamic Blind Source Separation Using Ensemble Learning. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'01), Washington D.C., USA, pp. 2750-2755.
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H. Lappalainen, A. Honkela. (2000). Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons. In M. Girolami, editor, Advances in Independent Component Analysis, pp. 93-121, Springer-Verlag.
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H. Lappalainen, J. Miskin. (2000). Ensemble Learning. In M. Girolami, editor, Advances in Independent Component Analysis, pp. 75-92, Springer-Verlag.
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H. Valpola, X. Giannakopoulos, A. Honkela, J. Karhunen. (2000). Nonlinear independent component analysis using ensemble learning: Experiments and discussion. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), Helsinki, Finland, pp. 351-356.
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H. Valpola. (2000). Nonlinear independent component analysis using ensemble learning: Theory. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), Helsinki, Finland, pp. 251-256.
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H. Valpola, P. Pajunen. (2000). Fast Algorithms for Bayesian Independent Component Analysis. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), Helsinki, Finland, pp. 233-237.
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H. Valpola. (2000). Unsupervised learning of nonlinear dynamic state-space models. Technical report A59, Lab of Computer and Information Science, Helsinki University of Technology, Finland.
H. Valpola. (2000). Bayesian Ensemble Learning for Nonlinear Factor Analysis. PhD thesis, Helsinki University of Technology, Espoo, Finland. Published in Acta Polytechnica Scandinavica, Mathematics and Computing Series No. 108.
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H. Lappalainen. (1999). Ensemble learning for independent component analysis. In Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), Aussois, France, pp. 7-12.
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