Workpage of Antti Honkela

D.Sc. (Tech.), Docent in Statistical Machine Learning (Aalto University)
Senior Researcher and PI, Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki

I will start a 5 year Academy Research Fellowship funded by the Academy of Finland at the University of Helsinki in September 2012.

My research interests include Bayesian machine learning and computational systems biology. I am interested in modelling nonlinear dynamical systems, especially modelling transcriptional regulation, and analysis of RNA-sequencing data and alternative splicing.

I am an action editor for the Journal of Machine Learning Research, Machine Learning Open Source Software section.

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.

Students

Hande Topa
Jussi Gillberg (together with Dr Jaakko Peltonen)
Karolis Uziela

Projects

Contact information

Room A344 at Department of Computer Science, Exactum, University of Helsinki
Telephone: +358-9-191 51253
Email: antti.honkela@hiit.fi
Mobile: +358 50 311 2483

Mailing address:

Helsinki Institute for Information Technology HIIT
Department of Computer Science
University of Helsinki
P.O. Box 68 (Gustaf Hällströmin katu 2b)
00014 University of Helsinki, Finland

Teaching

University of Helsinki:
Spring 2012
582637 Project in Probabilistic Models
Spring 2011
582637 Project in Probabilistic Models

Helsinki University of Technology:
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

Publications

Journal articles and book chapters

M. Titsias*, A. Honkela*, N. D. Lawrence, and M. Rattray.
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.
BMC Systems Biology (2012). To appear.

P. Glaus, A. Honkela*, and M. Rattray*.
Identifying differentially expressed transcripts from RNA-seq data with biological variation.
Bioinformatics (2012).
doi:10.1093/bioinformatics/bts260, arXiv:1109.0863 [q-bio.GN]

U. Remes, K. J. Palomäki, T. Raiko, A. Honkela, and M. Kurimo.
Missing-feature reconstruction with bounded nonlinear state-space model.
IEEE Signal Processing Letters 18(10):563-566 (2011).
doi:10.1109/LSP.2011.2163508

A. Honkela, P. Gao, J. Ropponen, M. Rattray, N. D. Lawrence.
tigre: Transcription factor Inference through Gaussian process Reconstruction of Expression for Bioconductor.
Bioinformatics 27(7):1026-1027 (2011).
doi:10.1093/bioinformatics/btr057

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).

Conference papers

B. H. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M. A. Weber, N. Ayache, and P. Golland.
A Generative Approach for Image-Based Modeling of Tumor Growth.
In Proceedings of the 22nd International Conference on Information Processing in Medical Imaging (IPMI 2011), Kloster Irsee, Germany.
Vol. 6801 of Lecture Notes in Computer Science, pp. 735-747, Springer-Verlag (2011).
doi:10.1007/978-3-642-22092-0_60

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).

Conference abstracts and presentations

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).

Theses

A. Honkela.
Advances in Variational Bayesian Nonlinear Blind Source Separation.
Doctoral thesis, Helsinki University of Technology, Espoo, Finland (2005).

A. Honkela.
Nonlinear Switching State-Space Models.
Master's thesis, Helsinki University of Technology, Espoo, Finland (2001).
(Browsable HTML version also available.)

Technical reports

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.

* Equal contribution.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Links

My personal home page (in Finnish)