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Teemu Roos: List of Publications

Books

  1. S. de Rooij, W. Kotlowski, J. Rissanen, P. Myllymäki, T. Roos, and K. Yamanishi (editors), (2012). Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2012), CWI, Amsterdam, ISBN 978-90-6196-563-3.

  2. J. Rissanen, P. Myllymäki, T. Roos, I. Tabus, and K. Yamanishi (editors), (2011). Proceedings of the Fourth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2011), Series of Publications C, Report C-2011-45, Deparment of Computer Science, University of Helsinki.

  3. P. Myllymäki, T. Roos, and T. Jaakkola (editors), (2010). Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), HIIT Publications 2010-2.

Refereed Journal Papers

  1. A. Carvalho, T. Roos, A. Oliveira, and P. Myllymäki, (2011). Discriminative learning of Bayesian networks via factorized conditional log-likelihood, Journal of Machine Learning Research 12(Jul):2181–2210.

  2. T. Silander, T. Roos, and P. Myllymäki, (2010). Learning locally minimax optimal Bayesian networks, International Journal of Approximate Reasoning 51(5):544–557.   preprint

  3. J. Rissanen, T. Roos, and P. Myllymäki, (2010). Model selection by sequentially normalized least squares, Journal of Multivariate Analysis 101(4):839–849.   preprint | R code

  4. T. Roos, P. Myllymäki, and J. Rissanen, (2009). MDL denoising revisited, IEEE Trans. Signal Processing, 57(9):3347–3360.   preprint | supplementary material | C code

  5. T. Roos and T. Heikkilä, (2009). Evaluating methods for computer-assisted stemmatology using artificial benchmark data sets, Literary and Linguistic Computing, 24(4):417–433, doi:10.1093/llc/fqp002. data-sets

  6. T. Roos, H. Wettig, P. Grünwald, P. Myllymäki, and H. Tirri, (2005). On discriminative Bayesian network classifiers and logistic regression, Machine Learning 59(3):267–296.

  7. T. Roos, P. Myllymäki, and H. Tirri, (2002). A statistical modeling approach to location estimation, IEEE Trans. Mobile Computing 1(1):59–69.

  8. T. Roos, P. Myllymäki, H. Tirri, P. Misikangas, and J. Sievänen, (2002). A probabilistic approach to WLAN user location estimation, Int. Journal of Wireless Information Networks 9(3):155–164.  Ekahau Inc.

Refereed Book Chapters

  1. P. Myllymäki, T. Roos, T. Silander, P. Kontkanen and H. Tirri, (2008). Factorized NML models, in Festschrift in Honor of Jorma Rissanen on the Occasion of his 75th Birthday, edited by P. Grünwald, P. Myllymäki, I. Tabus, M. Weinberger, and B. Yu.

  2. P. Kontkanen, P. Myllymäki, T. Roos, H. Tirri, K. Valtonen, H. Wettig, (2004). Probabilistic methods for location estimation in wireless networks, Chapter 11 in Emerging Location Aware Broadband Wireless Adhoc Networks, edited by R. Ganesh, S. Kota, K. Pahlavan and R. Agustí. Kluwer Academic Publishers.

Refereed Conference and Workshop Papers

  1. A. Oulasvirta, T. Roos, A. Modig, and L. Leppänen, (2013). Information capacity of full-body movements, to appear in Proc. 2013 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI-2013), ACM. Best paper honorable mention award.

  2. T. Roos and Y. Zou, (2011). Analysis of textual variation by latent tree structures, in Proc. IEEE International Conference on Data Mining (ICDM-2011), IEEE Press, pp. 567–576.

  3. T. Pulkkinen, T. Roos, and P. Myllymäki, (2011). Semi-supervised learning for WLAN positioning, in Proc. International Conference on Artificial Neural Networks (ICANN-2011), Lecture Notes in Computer Science 6791–6792, Springer, pp. 355–362.

  4. P.-H. Lai, T. Roos, and J. O'Sullivan, (2010). MDL hierarchical clustering for stemmatology, in Proc. 2010 IEEE International Symposium on Information Theory (ISIT-2010), IEEE Press, pp. 1403–1407.

  5. T. Merivuori and T. Roos, (2009). Some observations on the applicability of normalized compression distance to stemmatology, in Proc. 2nd Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2009).

  6. T. Silander, T. Roos, and P. Myllymäki, (2009). Locally minimax optimal predictive modeling with Bayesian networks, in Proc. 12th International Conference on Artificial Intelligence and Statistics (AISTATS-2009).

  7. T. Roos and B. Yu, (2009). Sparse Markov source estimation via transformed Lasso, in Proc. IEEE Information Theory Workshop 2009 (ITW-2009), IEEE Press, pp. 241–245.

  8. T. Silander, T. Roos, P. Kontkanen, and P. Myllymäki, (2008). Factorized NML criterion for learning Bayesian network structures, in Proc. 4th European Workshop on Probabilistic Graphical Models (PGM-2008). slides

  9. T. Roos, (2008). Monte Carlo estimation of minimax regret with an application to MDL model selection, in Proc. IEEE Information Theory Workshop 2008 (ITW-2008), IEEE Press.

  10. T. Roos, P. Grüwald, P. Myllymäki, and H. Tirri, (2006). Generalization to unseen cases, in Advances in Neural Information Processing Systems 18 (NIPS-2005), pp. 1129-1136.

  11. T. Roos, T. Heikkilä, and P. Myllymäki, (2006). A compression-based method for stemmatic analysis, in Proc. 17th European Conference on Artificial Intelligence (ECAI-2006), pp. 805–806.  extended version | challenge

  12. T. Roos, P. Grüwald, P. Myllymäki, and H. Tirri, (2005). Generalization to unseen cases, in Proc. 17th Belgian–Dutch Conference on Artificial Intelligence (BNAIC-2005), pp. 194–201. Best paper award.

  13. T. Roos, P. Myllymäki, and H. Tirri, (2005). On the behavior of MDL denoising, in Proc. 10th International Workshop on Artificial Intelligence and Statistics (AISTATS-2005), pp. 309-316. Erratum: Caption of Fig.4 should have sigma=5.0 instead of sigma=10.0.

  14. H. Wettig, P. Grünwald, T. Roos, P. Myllymäki, and H. Tirri, (2003). When discriminative learning of Bayesian network parameters is easy, in Proc. 18th International Conference on Artificial Intelligence (IJCAI-2003), pp. 491-498.

  15. H.Wettig, P. Grünwald, T.Roos, P. Myllymäki, H.Tirri, (2002). Supervised naive Bayes parameters, in STeP 2002 — Intelligence, The Art of Natural and Artificial: Proc. 10th Finnish Artificial Intelligence Conference, edited by P. Ala-Siuru and S. Kaski. Finnish Artificial Intelligence Society, pp. 72–83.

  16. H. Wettig, P. Grünwald, T. Roos, P. Myllymäki, and H. Tirri, (2002). Supervised learning of Bayesian network parameters made easy, in Proc. Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn-2002).

  17. P. Myllymäki, T. Roos, H. Tirri, P. Misikangas, and J. Sievänen, (2001). A probabilistic approach to WLAN user location estimation, in Proc. 3rd IEEE Workshop on Wireless Local Areas Networks, IEEE Press.

Other Publications (Invited/Unrefereed Papers, Theses, etc.)

  1. K. Watanabe, T. Roos, and P. Myllymäki, (2013). Non-Achievability of Asymptotic Minimax Regret without Knowledge of the Sample Size, in Proc. Information-Based Induction Sciences and Machine Learning (IBISML), Nagoya Institute of Technology, IEICE Technical Report IBISML2012-101, pp. 61–67.

  2. T. Roos and Y. Zou, (2013). Keep it simple stupid—On the effect of lower-order terms in BIC-like criteria, invited paper to appear in Proc. 2013 Information Theory and Applications Workshop, (ITA-2013).

  3. R. Eggeling, T. Roos, P. Myllymäki, and I. Grosse, (2012). Comparison of NML and Bayesian scoring criteria for learning parsimonious Markov models, to appear as an invited paper (extended abstract) in Proc. 5th Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2012).

  4. T. Roos, P. Myllymäki, and T. Jaakkola, (2012). Editorial: Special issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), International Journal of Approximate Reasoning 53(9):1303–1304.

  5. T. Roos, (2011). Yksinkertainen on kaunista: Okkamin partaveitsi tilastollisessa mallinnuksessa, Tietojenkäsittelytiede 32, 48–63.

  6. T. Roos, (2011). Introduction to Information-Theoretic Modeling, lecture notes, 33 pages.

  7. T. Roos, (2010). Terveisiä huippuyliopistoista, Tietojenkäsittelytiede 30, pp. 7–12.

  8. D.F. Schmidt and T. Roos, (2010). On the consistency of sequentially normalized least squares, invited paper (extended abstract) in Proc. 3rd Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-10), Tampere International Center for Signal Processing.

  9. T. Roos and B. Yu, (2009). Estimating sparse models from multivariate discrete data via transformed Lasso, invited paper in Proc. 2009 Information Theory and Applications Workshop (ITA-2009), IEEE Press.

  10. T. Roos and J. Rissanen, (2008). On sequentially normalized maximum likelihood models, invited paper in Proc. 1st Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2008), Tampere International Center for Signal Processing. slides | R code

  11. T. Roos, T. Silander, P. Kontkanen, and P. Myllymäki, (2008). Bayesian network structure learning using factorized NML universal models, invited paper in Proc. 2008 Information Theory and Applications Workshop (ITA-2008), IEEE Press.

  12. J. Rissanen, P. Grünwald, J. Heikkonen, P. Myllymäki, T. Roos, and J. Rousu, (2007). Editorial: information theoretic methods for bioinformatics, EURASIP Journal on Bioinformatics and Systems Biology. papers

  13. J. Rissanen, and T. Roos, (2007). Conditional NML universal models, invited paper in Proc. 2007 Information Theory and Applications Workshop (ITA-2007), IEEE Press, pp. 337–341.

  14. T. Roos, (2007). Statistical and Information-Theoretic Methods for Data Analysis, Ph.D. dissertation (summary part), Department of Computer Science, University of Helsinki. Classification Society Distinguished Dissertation Award Shortlist. abstract / tiivistelmä

  15. T. Roos, T. Heikkilä, R. Cilibrasi, P. Myllymäki, (2005). Compression-based stemmatology: a study of the Legend of St. Henry of Finland, Technical report HIIT-2005-3, Helsinki Institute for Information Technology HIIT.

  16. P. Kontkanen, P. Myllymäki, T. Roos, H. Tirri, K. Valtonen, H. Wettig, (2004). Topics in probabilistic location estimation in wireless networks, invited paper in Proc. 15th IEEE Symposium on Personal, Indoor and Mobile Radio Communications, IEEE Press.

  17. T. Roos, (2004). MDL regression and denoising, technical note, unpublished.

  18. H. Wettig, P. Grünwald, T. Roos, P. Myllymäki, H. Tirri, (2002). On supervised learning of Bayesian network parameters, Technical Report HIIT-2002-1, Helsinki Institute for Information Technology HIIT.

  19. T. Tonteri, (2001). A Statistical Modeling Approach to Location Estimation. Master's Thesis, Department. of Computer Science, University of Helsinki, May 2001.

Patents

  1. US Patent 7209752 (April 24, 2007). Error estimate concerning a target device's location operable to move in a wireless environment.

  2. US Patent 7228136 (June 5, 2007). Location estimation in wireless telecommunication networks.

Last updated on February 28, 2012

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