Information, Complexity and Learning


Photo: Veikko Somerpuro 2016

Leader: Teemu Roos

The Information, Complexity and Learning (ICL) research group is a part of the Cosco research group and studies the theory and applications of probabilistic models, especially graphical models. A particular area of interest is information theoretic methods. Application domains where we have continued collaboration include human–computer interaction, digital humanities, and bioinformatics.

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News and Events (see also Cosco News on the right)

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People

Senior Members

   

AVAILABLE ONLINE

 

B-Course

 

 

MDL-research

Teemu Roos,
PhD
Associate professor

Jorma Rissanen, DSc
Professor emeritus
 

 

Students

   

Ville Hyvönen
PhD student

Janne Leppä-aho
PhD student





Joonas Miettinen
PhD student

Elias Jääsaari
MSc student
 

   

Past members & alumni

Yuan Zou (PhD 2017)
Teemu Pitkänen (MSc 2016) >> Google Research Europe (Zurich)
Jussi Määttä (PhD 2016) >> Data Scientist @ Swap.com
Santeri Räisänen (research assistant 6/2014–12/2015)
Yang Zhao (research assistant 6/2014–12/2015)
Sotiris Tasoulis (postdoc 9/2013–8/2015) >> Liverpool John Moores University
Quan (Eric) Nguyen (MSc, research assistant 5/2013–8/2015) >> TU Eindhoven
Arttu Modig (MSc, research assistant 1/2012–5/2015) >> Finland's Slot Machine Association
Simo Linkola (MSc, research assistant 1/2011-4/2014) >> Computational Creativity Group
Nikolay Vasilev (intern summer 2013)

 

Recent Papers and Preprints

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

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.

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

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.

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, arXiv:1602.07863

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.

Y. Zhao, S. Tasoulis, and T. Roos (2016). Manifold visualization via short walks, EuroVis-2016.

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.

Y. Zou and T. Roos (2016). Sparse Logistic Regression with Logical Features, Proc. 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2016).

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).

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

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).

J. Määttä, D. F. Schmidt, and T. Roos, (2015). Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory, Scandinavian Journal of Statistics.

J. Tehrani, Q. Nguyen, and T. Roos, (2015). Oral fairy tale or literary fake? Investigating the origins of Little Red Riding Hood using phylogenetic network analysis, Digital Scholarship in the Humanities.

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).

K. Watanabe abd T. Roos, (2015). Achievability of asymptotic minimax regret by horizon-dependent and horizon-independent strategiesJMLR.

(Click here for older papers.)


Last updated on 27 Apr 2017 by Teemu Roos - Page created on 6 Sep 2012 by Petri Myllymäki