Researchers
PhD students
Research assistants
Visiting researchers
Graduated PhD students
Former Postdocs
Former group members (Master's students, visitors, etc)
Machine learning methodology
Applications
Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Temporal causal mediation through a point process: direct and indirect effects of healthcare interventions. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023). Available online
Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Causal Modeling of Policy Interventions From Treatment–Outcome Sequences. In Proceedings of the 40th International Conference on Machine Learning, PMLR 202. (ICML 2023). Available online
Raj, V., Cui, T., Heinonen, M., and Marttinen, P. (2023). Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. In Proceedings of 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6741-6763. (AISTATS 2023) Available online
Cui, T., Kumar, Y., Marttinen, P., and Kaski, S. (2022). Deconfounded Representation Similarity for Comparison of Neural Networks. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). Available online
Cui, T., Havulinna, A., Marttinen, P.*, and Kaski, S.* (2022). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 17(4):1121-1511. (*equal contribution) Available online
Rissanen, S. and Marttinen, P. (2021). A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 4207-4217. Available online
Järvenpää, M., Gutmann, M.U., Pleska, A., Vehtari, A., and Marttinen, P. (2019). Efficient acquisition rules for model-based approximate Bayesian computation. Bayesian Analysis, 14(2):595-622. Available online
Nikitin, A., Kossen, J., Gal, Y., and Marttinen, P. (2024). Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. In Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), accepted. Preprint
Dainese, N.*, Merler, M.*, Alakuijala, M., and Marttinen, P. (2024). Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search. In Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), accepted. (*equal contribution) Preprint
Spilsbury, S., Marttinen, P., and Ilin, A. (2024). Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), accepted. Preprint
Dainese, N., Marttinen, P., and Ilin, A. (2023). Reader: Model-based language-instructed reinforcement learning. The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Available online
Gao, Y., Moen, H., Koivusalo, S., Koskinen, M., and Marttinen, P. (2024). Query-Guided Self-Supervised Summarization of Nursing Notes. Proceedings of the Machine Learning for Health Symposium (ML4H 2024), accepted. Preprint
Honkamaa, J., Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., Ruusuvuori, P., and Marttinen, P. (2023). Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Analysis, 90:102940. Available online
Ji, S. and Marttinen, P. (2023). Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023). Available online
Marttinen, P. and Hanage, W.P. (2017). Speciation trajectories in recombining bacterial species. PLOS Computational Biology, 13(7):e1005640. Available online
Mostowy, R., Croucher, N.J., Andam, C.P., Corander, J., Hanage, W.P., and Marttinen, P. (2017). Efficient inference of recent and ancestral recombination within bacterial populations. Molecular Biology and Evolution, 34(5):1167-1182. Available online
Marttinen, P., Pirinen, M., Sarin, A.P., Gillberg, J., Kettunen, J., Surakka, I., Kangas, A.J., Soininen, P., O’Reilly, P.F., Kaakinen, M., Kähönen, M., Lehtimäki, T., Ala-Korpela, M., Raitakari, O.T., Salomaa, V., Järvelin, M.-R., Ripatti, S. and Kaski, S. (2014). Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinformatics, 30(14):2026-34. Available online
Corander, J. and Marttinen, P. (2006). Bayesian identification of admixture events using multi-locus molecular markers. Molecular Ecology, 15:2833-2843. Link
Ojala, F.*, Abdul Sater, M.R.*, Miller, L.G., McKinnell, J.A., Hayden, M.K., Huang, S.S., Grad, Y.H.+, and Marttinen, P.+ (2023). Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization. PLOS Computational Biology, accepted. (*/+ equal contribution) Preprint
Chewapreecha, C., Marttinen, P., Croucher, N.J.,Salter, S.J., Harris, S.R., Mather, A.E.,Hanage, W.P., Goldblatt, D., Nosten, F.H., Turner, C., Turner, P., Bentley, S.D. and Parkhill, J. (2014). Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. PLoS Genetics, 10(8):e1004547. Available online
Kashtan, N., Roggensack, S.E., Rodrigue, S., Thompson, J.W., Biller, S.J., Coe, A., Ding, H., Marttinen, P., Malmstrom, R.R., Stocker, R., Follows, M.J., Stepanauskas, R. and Chisholm, S.W. (2014). Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science, 344(6182): 416-420. Link
Chewapreecha, C., Harris, S.R., Croucher, N.J., Turner, C., Marttinen, P., Cheng, L., Pessia, A., Aanensen, D.M., Mather, A.E., Page, A.J., Salter, S.J., Harris, D., Nosten, F., Goldblatt, D., Corander, J., Parkhill, J., Turner, P. and Bentley, S.D. (2014). Dense genomic sampling identifies highways of pneumococcal recombination. Nature Genetics, 46: 305-309. Link
I have (co-)authored the following software for population genetics. For code related to later articles by the group, see the respective articles.
I am the responsible professor in the Major of Machine Learning, Data Science and Artificial Intelligence (Macadamia).
Courses I have taught: