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Teemu Roos: List of Publications

Peer-Reviewed Journal Articles

  1. V. Hyvönen, E. Jääsaari, T. Roos (2024). A multilabel classification framework for approximate nearest neighbor search, Journal of Machine Learning Research 25(46):1–51

  2. E. Roos, S. Heikkinen; K. Seppä, O. Pietiläinen, H. Ryynänen, M. Laaksonen, T. Roos, P. Knekt, S. Männistö, T. Härkönen, P. Jousilahti, S. Koskinen, J.G. Eriksson, N. Malila, O. Rahkonen, J. Pitkäniemi (2024). Pairwise association of key lifestyle factors and risk of solid cancers – A prospective pooled multi-cohort register study, Preventive Medicine Reports 2024 Jan 12;38:102607.

  3. A.S. Qureshi and T. Roos (2023). Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data sets, Neural Processing Letters 55:4461–4479

  4. E. Roos, K, Seppä, O. Pietiläinen, H. Ryynänen, S. Heikkinen, J.G. Eriksson, T. Härkönen, P. Jousilahti, P. Knekt, S. Koskinen, M. Laaksonen, S. Männistö, T. Roos, O. Rahkonen, N. Malila, J. Pitkäniemi, the METCA Study Group (2022). Pairwise association of key lifestyle factors and risk of colorectal cancer: A prospective pooled multi-cohort study, Cancer Reports, 2022;e1612

  5. J. Määttä, V. Bazaliy, J. Kimari, F. Djurabekova, K. Nordlund, T. Roos (2021). Gradient-based training and pruning of radial basis function networks with an application in materials physics, Neural Networks 133:123–131

  6. S. Tasoulis, N. Pavlidis, and T. Roos (2020). Nonlinear dimensionality reduction for clustering, Pattern Recognition 107:107508

  7. J. Leppä-aho, T. Silander, and T. Roos (2020). Bayesian network Fisher kernel for categorical feature spaces, Behaviormetrika 47:81-103.

  8. P. Grünwald and T. Roos (2019). Minimum description length revisited, Int. J. Mathematics for Industry 11(1):19300001

  9. T. Heikkilä and T. Roos (2018), Quantitative methods for the analysis of Medieval calendars, Digital Scholarship in the Humanities, 33(4):766–787.

  10. Y. Zou, J. Pensar, and T. Roos (2017). Representing local structure in Bayesian networks by Boolean functions, Pattern Recognition Letters 95(1):73–77.

  11. J. Leppä-aho, J. Pensar, T. Roos, and J. Corander (2017). Learning Gaussian graphical models with fractional marginal pseudo-likelihood, International Journal of Approximate Reasoning 83:21–42. preprint

  12. Y. Zou and T. Roos (2017). On model selection, Bayesian networks, and the Fisher information integral, New Generation Computing, 35(1) (Special Issue on AMBN 2015), January 2017. preprint

  13. L. Wang, S. Tasoulis, T. Roos, and J. Kangasharju (2016). Kvasir: Scalable provision of semantically relevant web content on big data framework, IEEE Transactions on Big Data 2(3):219–233.

  14. J. Määttä, D.F. Schmidt, and T. Roos (2016). Subset selection in linear regression using sequentially normalized least squares: Asymptotic theory, Scandinavian Journal of Statistics 43(2):382–395.

  15. J. Tehrani, Q. Nguyen, and T. Roos, (2016). Oral fairy tale or literary fake? Investigating the origins of Little Red Riding Hood using phylogenetic network analysis, Digital Scholarship in the Humanities 31(3):611–636.

  16. J. Määttä and T. Roos (2016). Maximum parsimony and the skewness test: A simulation study of the limits of applicability, PLOS ONE 11(4):e0152656.

  17. R. Eggeling, T. Roos, P. Myllymäki, I. Grosse (2015). Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data, BMC Bioinformatics 16:375.

  18. K. Watanabe and T. Roos, (2015). Achievability of asymptotic minimax regret by horizon-dependent and horizon-independent strategies, Journal of Machine Learning Research 16(Nov):2357–2375.

  19. 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.

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

  21. 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

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

  23. 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

  24. 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.

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

  26. 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.

Peer-Reviewed Book Chapters

  1. R.M. Ballardini, K. He, and T. Roos (2018), Digital distribution of AI-generated content: Authorship and inventorship in the age of artificial intelligence, in T. Pihlajarinne, J. Vesala, and O. Honkkila (editors), Online Distribution of Content in the EU, Edward Elgar Publishing Ltd.

  2. T. Roos (2016). Minimum Description Length Principle, in Sammut, C. and Webb G.I. (eds), Encyclopedia of Machine Learning and Data Mining, 2016.

  3. 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.

  4. 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.

Peer-Reviewed Conference and Workshop Articles

  1. E. Jääsaari, V. Hyvönen, T. Roos (2024). LoRANN: Low-rank matrix factorization for approximate nearest neighbor search, to appear at NeurIPS-2024 → github

  2. D. Holmberg, E. Clementi, T. Roos (2024), Regional ocean forecasting with hierarchical graph neural networks, to appear at NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning

  3. N. Pope, J. Kahila, J. Laru, H. Vartiainen, T. Roos, M. Tedre (2024). An educational tool for learning about social media tracking, profiling, and recommendation, ICALT-2024

  4. I. Bouri, F. Franssila, M.J. Alho, G. Cozzani, I. Zaitsev, M. Palmroth, and T. Roos (2023). Graph representation of the magnetic field topology in high-fidelity plasma simulations for machine learning applications, 2nd ICML Workshop on Machine Learning for Astrophysics

  5. V. Hyvönen, E. Jääsaari, T. Roos (2022). A multilabel classification framework for approximate nearest neighbor search, NeurIPS-2022

  6. F. Heintz and T. Roos (2021). Elements of AI – Teaching the basics of AI to everyone in Sweden, Proc 13th International Conference on Education and New Learning Technologies (EDULEARN2021)

  7. E. Jääsaari, V. Hyvönen, T. Roos (2019). Efficient autotuning of hyperparameters in approximate nearest neighbor search, in Proc. 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2019), Macau, China. C++ library with Python bindings

  8. U. Sheth, S. Dutta, M. Chaudhari, H. Jeong, Y. Yang, J. Kohonen, T. Roos, and P. Grover (2018). An application of storage-optimal MatDot codes for coded matrix multiplication: Fast k-nearest neighbors estimation, in Proc. IEEE Big Data Conference, pp. 1113–1120, IEEE Press

  9. J. Leppä-aho, S. Räisänen, X. Yang, and T. Roos (2018). Learning non-parametric Markov networks with mutual information, in Proc. 9th Int Conf on Probabilistic Graphical Models (PGM-2018), Prague, September 11–14, 2018. Best student paper honorable mention award.

  10. E. Jääsaari, J. Leppä-aho, T. Silander and T. Roos (2018). Minimax optimal Bayes mixtures for memoryless sources over large alphabets, in Proc. Int. Conf. on Algorithmic Learning Theory (ALT 2018)

  11. T. Silander, J. Leppä-aho, E. Jääsaari, and T. Roos (2018). Quotient normalized maximum likelihood criterion for learning Bayesian network structures, in Proc. 21st Int. Conf. on Artificial Intelligence and Statistics, PMLR 84:948-957 (AISTATS 2018) (+ supplementary PDF)

  12. V. Hyvönen, T. Pitkänen, S. Tasoulis, E. Jääsaari, R. Tuomainen, L. Wang, J. Corander, and T. Roos (2016). Fast nearest neighbor search through sparse random projections and voting, in Proc. 2016 IEEE International Conference on Big Data (IEEE Big-Data 2016), Washington DC, Dec. 5–8. C++ library (with Python bindings) | benchmarks

  13. Y. Zhao, S. Tasoulis, and T. Roos (2016). Manifold visualization via short walks, in E. Bertini, N. Elmqvist, and T. Wishchgoll (editors), Eurographics Conference on Visualization (EuroVis-2016), The Eurographics Association, pp. 85–89, DOI:10.2312/eurovisshort.20161166

  14. Y. Zou and T. Roos (2016). Sparse logistic regression with logical features, in J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, and R. Wang (editors), Proc. 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2016), LNAI 9652, Springer, pp. 316–327.

  15. J. Määttä and T. Roos (2016). Robust sequential prediction in linear regression with Student's t-distribution, in Proc. 14th International Symposium on Artificial Intelligence and Mathematics (ISAIM-2016).

  16. Y. Zou and T. Roos (2015). On model selection, Bayesian networks, and the Fisher information integral, in Proc. 2nd Workshop on Advanced Methodologies for Bayesian Networks (AMBN-2015).

  17. Q. Nguyen and T. Roos, (2015). Likelihood-based inference of phylogenetic networks from sequence data by PhyloDAG, in Proc. 2nd International Conference on Algorithms for Computational Biology (AlCoB-2015), LNBI 9199, Springer, pp. 126–140.

  18. J. Määttä, S. Siltanen, and T. Roos, (2014). A fixed-point image denoising algorithm with automatic window selection, in the 5th European Workshop on Visual Information Processing (EUVIP-2014), IEEE Press, pp. 1–6.

  19. S. Tasoulis, L. Cheng, N. Välimäki, N. Croucher, S. Harris, W. Hanage, T. Roos, and J. Corander, (2014). Random projection based clustering for population genomics, in IEEE International Conference on Big Data (IEEE BigData-2014), IEEE Press, pp. 675–682.

  20. A. Barron, T. Roos, and K. Watanabe, (2014). Bayesian properties of normalized maximum likelihood and its fast computation, in Proc. IEEE International Symposium on Information Theory (ISIT-2014), IEEE Press, pp. 1667–1671.

  21. M. Sherman, G. Clark, Y. Yang, S. Sugrim, A. Modig, J. Lindqvist, A. Oulasvirta, and T. Roos, (2014). User-generated free-form gestures for authentication: security and memorability, in Proc. 12th International Conference on Mobile Systems, Applications, and Services (MobiSys-2014), ACM Press, pp. 176–189.

  22. R. Eggeling, T. Roos, P. Myllymäki, I. Grosse, (2014). Robust learning of inhomogeneous PMMs, in Proc. 17th Conf on Artificial Intelligence and Statistics (AISTATS-2014), JMLR W&CP 33, pp. 229–237.

  23. K. Watanabe and T. Roos, (2013). Achievability of asymptotic minimax regret in online and batch prediction, in Proc. 5th Asian Conference on Machine Learning (ACML-2013), JMLR W&CP 29, pp. 181–196.

  24. A. Oulasvirta, T. Roos, A. Modig, and L. Leppänen, (2013). Information capacity of full-body movements, in Proc. 2013 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI-2013), ACM, pp. 1289–1298. Best paper honorable mention award.

  25. 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.

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

  27. 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.

  28. 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).

  29. 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).

  30. 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.

  31. 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

  32. 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.

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

  34. 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

  35. T. Roos, P. Grünwald, 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.

  36. 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.

  37. 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.

  38. 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.

  39. 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).

  40. 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 (Proceedings, Invited/Unrefereed Papers, Theses, etc.)

  1. P. Jacquet, J. Leppä-aho, and T. Roos (eds.) Proceeding of the The Tenth Workshop on Information Theoretic Methods in Science and Engineering, University of Helsinki, Department of Computer Science, Series of Publications B, Report B-2017-3.

  2. J. Rissanen, J. Leppä-aho, T. Roos, and P. Myllymäki (eds.). Proceeding of the The Ninth Workshop on Information Theoretic Methods in Science and Engineering, University of Helsinki, Department of Computer Science, Series of Publications B, Report B-2016-1.

  3. T. Heikkilä and T. Roos, (2016). Thematic Section on Studia Stemmatologica, Digital Scholarship in the Humanities 31(3):520–522, doi:10.1093/llc/fqw038.

  4. J. Rissanen, P. Harremoës, S. Forchhammer, T. Roos, and P. Myllymäki, (2015). Proceedings of the Eighth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2015), Series of Publications B, Report B-2015-1, Department of Computer Science, University of Helsinki.

  5. L. Wang, S. Tasoulis, T. Roos, and J. Kangasharju, (2015). Kvasir: Seamless integration of latent semantic analysis-based content provision into web browsing, demonstration paper in WWW 2015:251–254. Kvasir system

  6. J. Rissanen, P. Myllymäki, T. Roos, and N.P. Santhanam (editors), (2014). Proceedings of the Seventh Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-2014), Series of Publications B, Report B-2014-4, Department of Computer Science, University of Helsinki.

  7. T. Andrews, S. Linkola, T. Roos, and J. van Zundert, (2014). "Connecting the 'webs': Building interoperability into online services for stemmatology," abstract in DHBeneLux 2014, 12–13 June 2014, The Hague, Netherlands.

  8. 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.

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

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

  11. 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.

  12. 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.

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

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

  15. 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, Department of Computer Science, University of Helsinki.

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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  21. 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.

  22. 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

  23. 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.

  24. 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

  25. 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.

  26. 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.

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

  28. 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.

  29. 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 October 20, 2024.

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