Publications

This page lists the publications of the Probabilistic Machine Learning group formed in 2015. The group is a fusion of two former research groups from Aalto University, the Statistical Machine Learning and Bioinformatics group and the Bayesian Methodology group. For the earlier publications of these groups, please see below.

Order publications (2017-) by: Publication year Title First author

2016

• Muhammad Ammad-ud-din, Suleiman A.Khan, Disha Malani, Astrid Murumägi, Olli Kallioniemi, Tero Aittokallio and Samuel Kaski (2016). Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization. Bioinformatics. 32, 17, p. i455-i463. [Link] [Code]
• Homayun Afrabandpey, Tomi Peltola, Samuel Kaski. Regression Analysis in Small-n-Large-p Using Interactive Prior Elicitation of Pairwise Similarities. In FILM 2016, NIPS Workshop on Future of Interactive Learning Machines. [Link]
• Oswald Barral, Ilkka Kosunen, Tuukka Ruotsalo, Michiel M. Spapé, Manuel J. A. Eugster, Niklas Ravaja, Samuel Kaski, and Giulio Jacucci (2016). Extracting relevance and affect information from physiological text annotation. User Modeling and User-Adapted Interaction, 26:493-520, 2016. [Link]
• Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma and Samuel Kaski (2016). Modelling-based experiment retrieval: A case study with gene expression clustering. Bioinformatics, 32(9):1388-1394. [Link] [Preprint]
• Kerstin Bunte, Eemeli Leppäaho, Inka Saarinen, Samuel Kaski (2016). Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics. 32, 16, p. 2457-2463. [Link] [Preprint] [Code]
• Anna Cichonska, Juho Rousu, Pekka Marttinen, Antti J. Kangas, Pasi Soininen, Terho Lehtimäki, Olli T. Raitakari, Marjo-Riitta Järvelin, Veikko Salomaa, Mika Ala-Korpela, Samuli Ripatti, Matti Pirinen (2016). metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, 32(13):1981-1989, doi: 10.1093/bioinformatics. [Link]
• Pedram Daee, Tomi Peltola, Marta Soare and Samuel Kaski (2016). Probabilistic Expert Knowledge Elicitation of Feature Relevances in Sparse Linear Regression. In FILM 2016, NIPS Workshop on Future of Interactive Learning Machines. [Link]
• Pedram Daee, Joel Pyykkö, Dorota Glowacka, and Samuel Kaski (2016). Interactive intent modeling from multiple feedback domains. In Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI '16). ACM, Sonoma, California, USA, 71-75. [Link] [Preprint] [Reviews]
• Manuel J. A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé, Oswald Barral, Niklas Ravaja, Giulio Jacucci, Samuel Kaski (2016). Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals. Scientific Reports, 6:38580. [Link] [Preprint]
• Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu (2016). A robust convex formulation for ensemble clustering. In Proceedings of IJCAI-16, the Twenty-Fifth International Joint Conference on Artificial Intelligence , pp. 1476-1482, Palo Alto, CA, AAAI Press. [Link]
• Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski (2016). Multiple Output Regression with Latent Noise. Journal of Machine Learning Research, 17:1-35. [Link] [Code]
• Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki (2016). Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. In: Proceedings of AISTATS 2016, the 19th International Conference on Artificial Intelligence and Statistics, JMLR W&CP, pp. 732-740. [Link]
• Antti Kangasrääsiö, Yi Chen, Dorota Glowacka, Samuel Kaski (2016). Dealing with Concept Drift in Exploratory Search: An Interactive Bayesian Approach. IUI'16 Companion. ACM. [Pdf]
• Antti Kangasrääsiö, Yi Chen, Dorota Glowacka, and Samuel Kaski (2016). Interactive Modeling of Concept Drift and Errors in Relevance Feedback In 24th Conference on User Modeling, Adaptation and Personalization, UMAP '16. ACM, 2016. DOI: 10.1145/2930238.2930243. [Link] [Preprint]
• Antti Kangasrääsiö, Dorota Glowacka, and Samuel Kaski (2016). Personalization of Search Results using Interactive Intent Modeling. In ICML 2016 Workshop on Computational Frameworks for Personalization, 2016. Extended abstract. [Link]
• Antti Kangasrääsiö, Jarno Lintusaari, Kusti Skytén, Marko Järvenpää, Henri Vuollekoski, Michael Gutmann, Aki Vehtari, Jukka Corander and Samuel Kaski. ELFI: Engine for Likelihood-Free Inference. In NIPS 2016 Workshop on Advances in Approximate Bayesian Inference, 2016. Extended abstract. [Link] [Poster] [Code]
• Olli-Pekka Koistinen, Emile Maras, Aki Vehtari, Hannes Jónsson (2016). Minimum energy path calculations with Gaussian process regression. Nanosystems: Physics, Chemistry, Mathematics, 7(6):925-935. [Link]
• Lees, J.A., Vehkala, M., Välimäki, N., Harris, S.R., Chewapreecha, C., Croucher, N.J., Marttinen, P., Davies, M.R., Steer, A.C., Tong, S.Y.C., Honkela, A., Parkhill, J., Bentley, S.D., Corander, J. (2016). Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. Nature Communications, 7:12797, doi:10.1038/ncomms12797. [Link]
• Sieberts, S., Zhu, F., García-García, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., Falcao, A., Suver, C., Hoff, B., Balagurusamy, V., Dillenberger, D., Chaibub Neto, E., Norman, T., Aittokallio, T., Ammad-ud-din, M., Azencott, C.-A., Bellón, V., Boeva, V., Bunte, K., Chheda, H., Cheng, L., Corander, J., Dumontier, M., Goldenberg, A., Gopalacharyulu, P., Hajiloo, M., Hidru, D., Jaiswal, A., Kaski, S., Khalfaoui, B., Khan, S., Kramer, E., Marttinen, P., Mezlini, A., Molparia, B., Pirinen, M., Saarela, J., Samwald, M., Stoven, V., Tang, H., Tang, J., Torkamani, A., Vert, J.P., Wang, B., Wang, T., Wennerberg, K., Wineinger, N., Xiao, G., Xie, Y., Yeung, R., Zhan, X., Zhao, C., Greenberg, J., Kremer, J., Michaud, K., Barton, A., Coenen, M., Mariette, X., Miceli, C., Shadick, N., Weinblatt, M., de Vries, N, Tak, P., Gerlag, D., Huizinga, T.W.J., Kurreeman, F., Allaart, C., Bridges, S., Criswell, L., Moreland, L., Klareskog, L., Saevarsdottir, S., Padyukov, L., Gregersen, P., Friend, S., Plenge, R., Stolovitzky, G., Oliva, B., Guan, Y., and Mangravite, L. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7:12460, doi:10.1038/ncomms12460. [Link]
• Elina Numminen, Michael U. Gutmann, Mikhail Shubin, Pekka Marttinen, Guillaume Meric, Willem van Schaik, Teresa Coque, Fernando Baquero, Rob Willems, Samuel Sheppard, Edward Feil, William Hanage, and Jukka Corander (2016). The impact of host metapopulation structure on the population genetics of colonizing bacteria. Journal of Theoretical Biology, 396: 53-62. [Link]
• Marta Soare, Muhammad Ammad-ud-din, Samuel Kaski (2016). Regression with n → 1 by Expert Knowledge Elicitation. In the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16) pp. 734 - 739, 2016. [Link] [Preprint]
• Suleiman A. Khan, Eemeli Leppäaho and Samuel Kaski (2015). Bayesian multi-tensor factorization. Machine Learning, 105(2):233-253, doi:10.1007/s10994-016-5563-y. [Link] [Preprint] [Code]
• Jarno Lintusaari, Michael U. Gutmann, Samuel Kaski and Jukka Corander (2016). On the identifiability of transmission dynamic models for infectious diseases. GENETICS. March 7, 2016 vol. 202 no. 3 911-918; DOI: 10.1534/genetics.115.180034 [Link] [Preprint]
• Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander (2016). Fundamentals and Recent Developments in Approximate Bayesian Computation. Systematic Biology, 66 (1): e66-e82. [Link]
• Teppo Niinimäki, Pekka Parviainen and Mikko Koivisto (2016). Structure Discovery in Bayesian Networks by Sampling Partial Orders. In Journal of Machine Learning Research. 17(57):1-47. [Link]
• Pekka Parviainen and Samuel Kaski (2016). Bayesian Networks for Variable Groups. In Proceedings of the Eighth International Conference on Probabilistic Graphical Models (PGM). JMLR Workshop and Conference Proceedings, 52: 380-391. [Link]
• Juho Piironen and Aki Vehtari (2016). Projection predictive model selection for Gaussian processes. In 2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). [Link] [Preprint]
• Jarmo Rantonen, Jaro Karppinen, Aki Vehtari, Satu Luoto, Eira Viikari-Juntura, Markku Hupli, Antti Malmivaara and Simo Taimela (2016). Cost-effectiveness of providing patients with information on managing mild low-back symptoms. A controlled trial in an occupational health setting. BMC Public Health, 16:316. DOI: 10.1186/s12889-016-2974-4. [Link]
• Alan Saul, James Hensman, Aki Vehtari, Neil Lawrence (2016). Chained Gaussian processes. Journal of Machine Learning Research: Workshop and Conference Proceedings (AISTATS 2016 Proceedings), 51:1431-1440. [Link] [Preprint] [Code]
• Aki Vehtari, Andrew Gelman and Jonah Gabry (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. In Statistics and Computing, doi:10.1007/s11222-016-9696-4. [Link] [Preprint] [Matlab code] [R code]
• Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula and Ole Winther (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. Journal of Machine Learning Research, 17(103):1-38. [Link] [Preprint] [Code]
• Seppo Virtanen, Homayun Afrabandpey, and Samuel Kaski. (2016). Visualizations relevant to the user by multi-view latent variable factorization. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 2464-2468. [Link] [Preprint]

2015

• Salvatore Andolina, Khalil Klouche, Jaakko Peltonen, Mohammad Hoque, Tuukka Ruotsalo, Diogo Cabral, Arto Klami, Dorota Glowacka, Patrik Floreen, and Giulio Jacucci. IntentStreams: Smart Parallel Search Streams for Branching Exploratory Search. In Proceedings of ACM IUI 2015, The 20th ACM Conference on Intelligent User Interfaces, pp. 300-305, 2015. [link] [YouTube video of the system]
• Oswald Barral, Manuel J.A. Eugster, Tuukka Ruotsalo, Michiel Sovijärvi-Spapé, Ilkka Kosunen, Niklas Ravaja, Samuel Kaski, and Giulio Jacucci. Exploring peripheral physiology as a predictor of perceived relevance in information retrieval. In Proceedings of the 20th International Conference on Intelligent User Interfaces, pages 389–399, New York, NY, 2015. ACM. PDF (3 MB) [More info] [See also: dl.acm.org ...]
• Ella Bingham, Samuel Kaski, Jorma Laaksonen, and Jouko Lampinen, editors. Advances in Independent Component Analysis and Learning Machines. Academic Press, London, 2015. [More info]
• Paul Blomstedt and Jukka Corander. Posterior predictive comparisons for the two-sample problem. Communications in Statistics – Theory and Methods, 44(2):376–389, 2015. [More info] [See also: dx.doi.org ...]
• Paul Blomstedt, Jing Tang, Jie Xiong, Christian Granlund, and Jukka Corander. A Bayesian predictive model for clustering data of mixed discrete and continuous type. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):489–498, 2015. [More info] [See also: dx.doi.org ...]
• Manuel J. A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé, Ilkka Kosunen, Oswald Barral, Niklas Ravaja, Giulio Jacucci, and Samuel Kaski. Predicting relevance of text from neuro-physiology. In SIGIR 2015 workshop ``NeuroIR 2015 – Neuro-Physiological Methods in IR Research'', 2015. Extended abstract. [More info] [See also: sites.google.com ...]
• Dario Gasbarra, Elja Arjas, Aki Vehtari, Rémy Slama and Niels Keiding (2015). The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective. Lifetime Data Analysis, 21(4):594-625. DOI:10.1007/s10985-015-9333-0. [Link]
• Roland C. Grafström, Penny Nymark, Vesa Hongisto, Ola Spjuth, Rebecca Ceder, Egon Willighagen, Barry Hardy, Samuel Kaski, and Pekka Kohonen. Toward the replacement of animal experiments through the bioinformatics-driven analysis of 'omics' data from human cell cultures. ATLA: Alternatives to Laboratory Animals, 43:325-332, 2015.
• Antti Honkela*, Jaakko Peltonen*, Hande Topa, Iryna Charapitsa, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, and Magnus Rattray. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays. Proceedings of the National Academy of Sciences of the United States of America, Early Edition, 2015. (* A.H. and J.P. contributed equally to this work.) [Online article]
• Antti Kangasrääsiö, Dorota Glowacka, and Samuel Kaski. Improving controllability and predictability of interactive recommendation interfaces for exploratory search. In Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI '15, pages 247–251, New York, NY, USA, 2015. ACM. PDF (860 kB) [More info] [See also: doi.acm.org ...]
• Jukka-Pekka Kauppi, Melih Kandemir, Veli-Matti Saarinen, Lotta Hirvenkari, Lauri Parkkonen, Arto Klami, Riitta Hari, and Samuel Kaski. Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. NeuroImage, 112:288–298, 2015. PDF (1 MB) [More info]
• Arto Klami, Seppo Virtanen, Eemeli Leppäaho, and Samuel Kaski. Group factor analysis. IEEE Transactions on Neural Networks and Learning Systems, 26(9):2136-2147, 2015. [Link] [Code] [PDF] [Preprint]
• Janne H. Korhonen and Pekka Parviainen. Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number. Advances in Neural Information Processing Systems, 28, 622-630, 2015. [Link] [Code] [Repository]
• Pekka Marttinen, Nicholas J. Croucher, Michael U. Gutmann, Jukka Corander, William P. Hanage (2015). Recombination produces coherent bacterial species clusters in both core and accessory genomes. Microbial Genomics, 1, doi:10.1099/mgen.0.000038. [Link]
• Samu H. P. Mäntyniemi, Rebecca E. Whitlock, Tommi A. Perälä, Paul A. Blomstedt, Jarno P. Vanhatalo, Margarita María Rincón, Anna K. Kuparinen, Henni P. Pulkkinen and O. Sakari Kuikka. General state-space population dynamics model for Bayesian stock assessment. ICES Journal of Marine Science, 72(8):2209-2222, 2015. [Link]
• Tuukka Ruotsalo, Giulio Jacucci, Petri Myllymäki, and Samuel Kaski. Interactive intent modeling: Information discovery beyond search. Communications of the ACM, 58(1):86–92, 2015. PDF (3 MB) [More info] [See also: dl.acm.org ...]
• Tuukka Ruotsalo, Jaakko Peltonen, Manuel J. A. Eugster, Dorota Glowacka, Aki Reijonen, Giulio Jacucci, Petri Myllymäki, and Samuel Kaski. SciNet: Interactive intent modeling for information discovery. In Proceedings of SIGIR'15, the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1043–1044. ACM, New York, NY, 2015. PDF (1 MB) [More info]
• Sohan Seth and Manuel J. A. Eugster. Probabilistic archetypal analysis. Machine Learning, 102(1):85-113, 2015. DOI:10.1007/s10994-015-5498-8 [Link] [code]
• Ikram Ullah, Pekka Parviainen, and Jens Lagergren. Species Tree Inference Using a Mixture Model. Molecular Biology and Evolution, 32(9):2469-2482, 2015. [Link]
• Chirayu Wongchockprasitti, Jaakko Peltonen, Tuukka Ruotsalo, Payel Bandyopadhyay, Giulio Jacucci and Peter Brusilovsky. User Model In a Box: Cross-System User Model Transfer for Resolving Cold Start Problems. In Proceedings of UMAP'15, The 23rd Conference on User Modelling, Adaptation and Personalization, pages 289-301, Springer, 2015. [link] [slides] [presentation in UMAP conference navigator]
• Zhirong Yang, Jaakko Peltonen, and Samuel Kaski. Majorization-minimization for manifold embedding. In Guy Lebanon and S. V. N. Vishwanathan, editors, Proceedings of AISTATS-2015, the Eighteenth International Conference on Artificial Intelligence and Statistics, JMLR W&CP, pages 1088–1097. JMLR, 2015. [More info] [See also: jmlr.org ...]

2014 and earlier

Publications of the former Statistical Machine Learning and Bioinformatics group

Publications of the former Bayesian Methodology group

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.