Since January 2011 I am now at Helsinki Institute for Information Technology HIIT, University of Helsinki. Please see my new home page, where you will be taken automatically in 5 seconds.

Docent in Statistical Machine Learning

Postdoctoral research fellow (funded by a Postdoctoral Researcher's
Project of the Academy of Finland)

I am affiliated with the Bayes group of the Adaptive Informatics Research Centre. My research interests include Bayesian machine learning and approximate inference in particular. I am interested in modelling nonlinear dynamical systems with applications to bioinformatics, especially modelling transcription regulation.

I have recently returned from a visit to the University of Manchester, where I was working with Neil Lawrence and Magnus Rattray.

The tigre package implementing the transcription factor target ranking method from our recent PNAS paper is available in Bioconductor.

Other free software tools I have created are available on the pages of the Bayes and IVGA groups, as well as with the Manchester group.

I completed my doctoral thesis, "Advances in Variational Bayesian Nonlinear Blind Source Separation", in spring 2005. My master's thesis, "Nonlinear Switching State-Space Models", is also available here. In addition to the browseable HTML version, the thesis is available as PostScript (659 kB) and PDF (844 kB) versions.

Suleiman Ali Khan (together with Prof. Samuel Kaski)

Veli Peltola

- SYNERGY - Systems approach to gene regulation biology through nuclear receptors, a transnational ERASysBio+ project

Email: antti.honkela@tkk.fi

- Spring 2009
- T-61.6070 Special Course in Bioinformatics I: Learning and Inference in Dynamic Models of Biological Networks
- Autumn 2008
- T-61.5110 Modelling of biological networks
- 2006-2007
- Scientific coordinator of the International Master's Programme in Machine Learning and Data Mining - Macadamia
- Spring 2007
- T-61.152 Informaatiotekniikan seminaari: tiedonhaku (Seminar in Computer and Information Science: Information retrieval)
- Autumn 2006
- T-61.6010 Special Course in Computer and Information Science I: Gaussian Processes for Machine Learning
- Spring 2006
- T-61.152 Informaatiotekniikan seminaari: ydinfunktiomenetelmät (Seminar in Computer and Information Science: Kernel methods)
- 2005-2006
- Coordinator for the lab of Computer and Information Science for the department of Computer Science and Engineering B.Sc. seminar (Kandidaattiseminaari)
- Autumn 2004
- T-61.182 Information Theory and Machine Learning
- Autumn 2001
- T-61.181 Independent Component Analysis

A. Honkela, T. Raiko, M. Kuusela, M. Tornio, and J. Karhunen.

Approximate Riemannian conjugate
gradient learning for fixed-form variational Bayes.

Journal of Machine Learning Research 11(Nov):3235-3268 (2010).

(Also available: Pre-print pdf)

A. Honkela, C. Girardot, E. H. Gustafson, Y.-H. Liu, E. E. M. Furlong,
N. D. Lawrence and M. Rattray.

Model-based method for transcription factor target identification with
limited data.

Proc. Natl. Acad. Sci. U S A 107(17):7793-7798 (2010).

doi:10.1073/pnas.0914285107

P. Gao, A. Honkela, M. Rattray, and N. D. Lawrence.

Gaussian
process modelling of latent chemical species: applications to
inferring transcription factor activities.

Bioinformatics 24(16):i70-i75 (2008).

Appeared in Proceedings of ECCB 2008.

doi:10.1093/bioinformatics/btn278

A. Honkela, J. Seppä, and E. Alhoniemi.

Agglomerative Independent
Variable Group Analysis.

Neurocomputing 71(7-9):1311-1320 (2008).

Appeared in Special Issue for the 15th European Symposium on Artificial
Neural Networks (ESANN 2007).

doi:10.1016/j.neucom.2007.11.024

A. Honkela, H. Valpola, A. Ilin, and J. Karhunen.

Blind Separation of Nonlinear
Mixtures by Variational Bayesian Learning.

Digital Signal Processing 17(5):914-934 (2007).

Appeared in Special Issue on Bayesian Source Separation.

doi:10.1016/j.dsp.2007.02.009

E. Alhoniemi, A. Honkela, K. Lagus, J. Seppä, P. Wagner, and
H. Valpola.

Compact Modeling of
Data Using Independent Variable Group Analysis.

IEEE Transactions on Neural Networks 18(6):1762-1776 (2007).

doi:10.1109/TNN.2007.900809

A. Honkela and H. Valpola.

Variational learning and bits-back coding: an
information-theoretic view to Bayesian learning.

IEEE Transactions on Neural Networks 15(4):800-810 (2004).

Appeared in Special Issue on Information Theoretic Learning.

doi:10.1109/TNN.2004.828762

A. Honkela, H. Valpola and J. Karhunen.

Accelerating Cyclic Update Algorithms for Parameter Estimation by
Pattern Searches.

Neural Processing Letters 17(2):191-203 (2003).

doi:10.1023/A:1023655202546

H. Valpola, E. Oja, A. Ilin, A. Honkela and J. Karhunen.

Nonlinear Blind Source Separation by Variational Bayesian Learning.

IEICE Transactions on Fundamentals of Electronics,
Communications and Computer Sciences E86-A(3):532-541 (2003).

Publisher electronic edition

H. Lappalainen and A. Honkela.

Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons.

In M. Girolami, editor, Advances in
Independent Component Analysis, pp. 93 - 121, Springer (2000).

Also available as a PostScript version (420 kb).

V. Peltola and A. Honkela. Variational Inference and Learning for Non-Linear State-Space Models with State-Dependent Observation Noise. In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, pp. 190-195 (2010).

A. Honkela, M. Milo, M. Holley, M. Rattray, and N. D. Lawrence. Ranking of Gene Regulators through Differential Equations and Gaussian Processes. In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, pp. 154-159 (2010).

M. Kuusela, T. Raiko, A. Honkela, and J. Karhunen. A Gradient-Based Algorithm Competitive with Variational Bayesian EM for Mixture of Gaussians. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, Georgia, June 15-19 (2009).

A. Honkela, M. Tornio, T. Raiko, and J. Karhunen.
Natural Conjugate Gradient in
Variational Inference.
In Proceedings of the 14th International Conference on
Neural Information Processing (ICONIP 2007), Kitakyushu, Japan.
Vol. 4985 of Lecture Notes in Computer Science, pp. 305-314,
Springer-Verlag (2008).

doi:10.1007/978-3-540-69162-4_32

A. Honkela, J. Seppä, and E. Alhoniemi. Agglomerative Independent Variable Group Analysis. In Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), Bruges, Belgium, pp. 55-60 (2007).

M. Tornio, A. Honkela, and J. Karhunen. 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 (2007).

J. Nikkilä, A. Honkela, and S. Kaski. Exploring the Independence of Gene Regulatory Modules. In J. Rousu, S. Kaski, and E. Ukkonen, editors, Proc. Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, pp. 131-136 (2006).

T. Raiko, M. Tornio, A. Honkela and J. Karhunen.
State Inference in Variational Bayesian Nonlinear State-Space Models.
In Proceedings of the Sixth
International Conference Independent Component Analysis and Blind
Signal Separation (ICA 2006), Charleston, South Carolina, USA.
Vol. 3889 of Lecture
Notes in Computer Science, pp. 222 - 229, Springer-Verlag (2006).

doi:10.1007/11679363_28

M. Harva, T. Raiko, A. Honkela, H. Valpola and J. Karhunen.
Bayes Blocks: An Implementation of the Variational Bayesian
Building Blocks Framework.
In Proceedings of the 21st Conference on Uncertainty in
Artificial Intelligence, Edinburgh, UK, pp. 259 - 266 (2005).

K. Lagus, E. Alhoniemi, J. Seppä, A. Honkela and P. Wagner.
Independent Variable Group Analysis in Learning Compact Representations
for Data. In Proceedings of the International and Interdisciplinary
Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05),
Helsinki, Finland, pp. 49 - 56 (2005).

A. Honkela, T. Östman, R. Vigário.
Empirical evidence of the linear nature of magnetoencephalograms.
In Proceedings of the 13th European Symposium on Artificial Neural
Networks (ESANN 2005), Bruges, Belgium, pp. 285 - 290 (2005).

A. Honkela and H. Valpola. Unsupervised Variational Bayesian Learning
of Nonlinear Models. In L. Saul, Y. Weiss, and L. Bottou, editors,
Advances in Neural Information Processing Systems 17, pp. 593 - 600,
The MIT Press (2005).

A. Ilin and A. Honkela.
Postnonlinear Independent Component Analysis
by Variational Bayesian Learning. In Proceedings of the
Fifth International Conference Independent Component Analysis and
Blind Signal Separation (ICA 2004), Granada, Spain. Vol. 3195 of
Lecture Notes in Computer Science, pp. 766 - 773, Springer-Verlag (2004).

Publisher electronic edition

A. Honkela, S. Harmeling, L. Lundqvist and H. Valpola.
Using Kernel
PCA for Initialisation of Variational Bayesian Nonlinear Blind Source
Separation Method. In Proceedings of the Fifth
International Conference Independent Component Analysis and Blind
Signal Separation (ICA 2004), Granada, Spain. Vol. 3195 of Lecture
Notes in Computer Science, pp. 790 - 797, Springer-Verlag (2004).

Publisher electronic edition

A. Honkela.
Approximating Nonlinear Transformations of Probability
Distributions for Nonlinear Independent Component Analysis.
In Proceedings of the 2004 IEEE International Joint Conference on
Neural Networks (IJCNN 2004), Budapest, Hungary, pp. 2169 - 2174 (2004).

V. Siivola and A. Honkela.
A State-Space Method for Language Modeling.
In Proceedings of the IEEE Workshop on Automatice Speech
Recognition and Understanding (ASRU 2003), St. Thomas,
U.S. Virgin Islands, pp. 548 - 553 (2003).

A. Honkela and H. Valpola.
On-line Variational Bayesian Learning.
In Proceedings of the Fourth International Symposium on
Independent Component Analysis and Blind Signal Separation
(ICA 2003), Nara, Japan, pp. 803 - 808 (2003).

A. Honkela.
Speeding Up Cyclic Update Schemes by Pattern Searches.
In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02), Singapore, pp. 512 - 516 (2002).

H. Valpola, A. Honkela, and J. Karhunen.
An Ensemble Learning Approach to Nonlinear Dynamic Blind Source Separation Using State-Space Models.
In Proceedings of the International Joint Conference on Neural Networks (IJCNN'02), Honolulu, Hawaii, USA, pp. 460 - 465 (2002).

H. Valpola, A. Honkela, and J. Karhunen.
Nonlinear Static and Dynamic Blind Source Separation Using Ensemble Learning.
In Proceedings of the International Joint Conference on Neural Networks (IJCNN'01), Washington D.C., USA, pp. 2750 - 2755 (2001).

A. Honkela and J. Karhunen.
An Ensemble Learning Approach to Nonlinear Independent Component Analysis.
In Proceedings of the European Conference on
Circuit Theory and Design (ECCTD'01), Espoo, Finland,
pp. I-41 - 44 (2001).

H. Valpola, X. Giannakopoulos, A. Honkela, and J. Karhunen.
Nonlinear Independent Component Analysis
Using Ensemble Learning: Experiments and Discussion.
In Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA 2000, Helsinki, Finland,
pp. 351 - 356 (2000).

H. Lappalainen, A. Honkela, X. Giannakopoulos, and J. Karhunen.
Nonlinear Source Separation Using Ensemble Learning and MLP Networks.
In Proceedings of the Symposium 2000 on Adaptive Systems for
Signal Processing, Communications, and Control (AS-SPCC), Lake Louise, Alberta,
Canada, pp. 187 - 192 (2000).

A. Honkela, M. Tornio, and T. Raiko. 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 (2006).

A. Honkela, M. Harva, T. Raiko, H. Valpola, and J. Karhunen. Bayes Blocks: A Python Toolbox for Variational Bayesian Learning. In NIPS*2006 Workshop on Machine Learning Open Source Software, Whistler, B.C., Canada (2006).

A. Honkela. Distributed Bayes Blocks for Variational Bayesian Learning. In Conference on High Performance Computing for Statistical Inference, Dublin, Ireland (2006).

A. Honkela, M. Tornio, T. Raiko, and J. Karhunen. Natural Conjugate Gradient in Variational Inference. Publications in Computer and Information Science E10, Helsinki University of Technology, Espoo, Finland, 2007.

H. Valpola and A. Honkela. Hyperparameter Adaptation in Variational Bayes for the Gamma Distribution. Publications in Computer and Information Science E6, Helsinki University of Technology, Espoo, Finland, 2006.

E. Alhoniemi, A. Honkela, K. Lagus, J. Seppä, P. Wagner, and H. Valpola. Compact Modeling of Data Using Independent Variable Group Analysis. Publications in Computer and Information Science E3, Helsinki University of Technology, Espoo, Finland, 2006.

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