Tutkimus

Tältä sivulta löydät hankkeen julkaisut teemoittain.

Hankkeen julkaisut

Koneoppiminen ja päätelmien luotettavuus
  • Alakuijala, M., McLean, R., Woungang, I., Farsad, N., Kaski, S., Marttinen, P., and Yuan, K. (2025). Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. Transactions on Machine Learning Research (TMLR). Linkki artikkeliin.
  • Hizli, C., Yildiz, C., Bethge, M., and Marttinen, P. (2025). Identifying latent state transition in non-linear dynamical systems. In Proceedings of The Thirteenth International Conference on Learning Representations (ICLR 2025). Linkki artikkeliin.
  • 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). Linkki artikkeliin.
  • Honkamaa, J. and Marttinen, P. (2024). SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration. Journal of Machine Learning for Biomedical Imaging. Linkki artikkeliin.
  • 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). Linkki artikkeliin.
  • 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). Linkki artikkeliin.
  • 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). Linkki artikkeliin.
  • Pöllänen, A. and Marttinen, P. (2024). Identifiable causal inference with noisy treatment and no side information. Transactions on Machine Learning Research (TMLR). Linkki artikkeliin.
  • Kumar, Y. and Marttinen, P. (2024). Improving Medical Multi-modal Contrastive Learning with Expert Annotations. The 18th European Conference on Computer Vision (ECCV 2024). Linkki artikkeliin.
  • Haitsiukevich, K., Poyraz, O., Marttinen, P., and Ilin, A. (2024). Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems. IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2024). Linkki artikkeliin. 
  • Merler, M.*, Hatsiukevich, K.*, Dainese, N.*, and Marttinen, P. (2024). In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 589-606. (*equal contribution). Linkki artikkeliin.
  • Hotta, S., Kytö, M., Koivusalo, S., Heinonen, S., and Marttinen, P. (2024). Optimizing postprandial glucose prediction through integration of diet and exercise: leveraging transfer learning with imbalanced patient data. PLOS ONE, 19(8):e0298506. Linkki artikkeliin.
  • Ji, S., Sun, W., Li, X., Dong, H., Taalas, A., Zhang, Y., Wu, H., Pitkänen, E., and Marttinen, P. (2024). A Unified Review of Deep Learning for Automated Medical Coding. ACM Computing Surveys. Linkki artikkeliin.
  • Kumar, Y., Ilin, A., Salo, H., Kulathinal, S., Leinonen, M.K., and Marttinen, P. (2024). Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models. IEEE Transactions on Artificial Intelligence. Linkki artikkeliin.
  • Gao, Y., Ji, S., and Marttinen, P. (2024). Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9787-9898. Linkki artikkeliin.
  • Dainese, N., Ilin, A., and Marttinen, P. (2024). Can docstring reformulation with an LLM improve code generation? In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 296–312. Linkki artikkeliin. 
  • Holster, T., Ji, S., and Marttinen, P. (2023). Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation. The European Journal of Health Economics. Linkki artikkeliin.
  • Odnoblyudova, A., Hızlı, C., John, S.T., Cognolato, A., Juuti, A., Särkkä, S., Pietiläinen, K., and Marttinen, P. (2023). Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. Proceedings of the 3rd Machine Learning for Health Symposium 2023, PMLR 225:428-444 (ML4H 2023). Linkki artikkeliin.
  • Poyraz, O. and Marttinen, P. (2023). Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. In Proceedings of the 3rd Machine Learning for Health Symposium 2023, PMLR 225:461-479 (ML4H 2023). Linkki artikkeliin. 
  • Dainese, N., Marttinen, P., and Ilin, A. (2023). Reader: Model-based language-instructed reinforcement learning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), pages 16583–16599. Linkki artikkeliin.
  • 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). Linkki artikkeliin. 
  • Kytö, M., Koivusalo, S., Tuomonen, H., Strömberg, L., Ruonala, A., Marttinen, P., Heinonen, S., and Jacucci, G. (2023). Supporting Management of Gestational Diabetes with Comprehensive Self-Tracking: Mixed-Method Study of Wearable Sensors. JMIR Diabetes. Linkki artikkeliin. 
  • 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. Linkki artikkeliin.
  • 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). Linkki artikkeliin. 
  • Raj, V., Cui, T., Heinonen, M., and Marttinen, P. (2023). Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. The Proceedings of 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6741-6763. (AISTATS 2023). Linkki artikkeliin.
  • Karami, S., Saberi-Movahed, F., Tiwari, P., Marttinen, P., and Vahdati, S. (2023). Unsupervised Feature Selection Based on Variance-Covariance Subspace Distance. Neural Networks, 166:188-203. Linkki artikkeliin.
  • 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). Linkki artikkeliin.
  • Sun, W., Ji, S., Denti, T., Moen, H., Kerro, O., Rannikko, A., Marttinen, P., and Koskinen, M. (2023). Weak Supervision and Clustering-based Sample Selection for Clinical Named Entity Recognition. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Lecture Notes in Computer Science, 14174. (ECML PKDD 2023). Linkki artikkeliin.
  • Oghabian, A., van der Kolk, B.W., Marttinen, P., Valsesia, A., Langin, D., Saris, W.H., Astrup, A., Blaak, E.E., and Pietiläinen, K.H. (2023). Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity. PeerJ, 11:e15100. Linkki artikkeliin.
  • Gao, Y., Ji, S., Zhang, T., Tiwari, P., and Marttinen, P. (2022). Contextualized graph embeddings for adverse drug event detection. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022).Linkki artikkeliin.
  • 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). Linkki artikkeliin. 
  • 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). Linkki artikkeliin.
  • Cui, T., Havulinna, A., Marttinen, P., and Kaski, S. (2021). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 1-31. Linkki artikkeliin.
  • Sun, W, Ji, S., Cambria, E., and Marttinen, P. (2021). Multitask recalibrated aggregation network for medical code prediction. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Linkki artikkeliin.
Yksityisyys ja synteettinen data
  • Jälkö, J & Lagerspetz, E & Haukka, J & Tarkoma, S & Honkela, A & Kaski, S (2021) Privacy-preserving data sharing via probabilistic modeling. Linkki artikkeliin.
Oikeudelliset ja eettiset vaatimukset datan käytölle
  • Fritzsche, M., Akyüz, K., Abadía, M.C., McLennan, S., Marttinen, P. Mayrhofer, M.T., and Buyx, A.M. (2023). Ethical layering in AI-driven polygenic risk scores – new complexities, new challenges. Frontiers in Genetics, 14. Linkki artikkeliin.
  • Tupasela, A. (2022). Data Ethics and the Testbed Nation. In E. di Nucci, J-Y Lee & I.A. Wagner (Eds.), The Rowan & Littlefield Book of Bioethics. Rowan & Littlefield Publishers. (forthcoming)
  • Tarkkla H & Snell K (2022) ‘The window of opportunity is closing’—advocating urgency and unity. Humanities and Social Sciences Communications 9, article no 324 Linkki artikkeliin
  • Snell K., Tarkkala H. & Tupasela A. (2022) A solidarity paradox – welfare state data in global health data economy. Health. Linkki artikkeliin.
  • Snell, K. (2020) Henkilökohtaisten terveystietojen hyödyntämisen oikeutukset: Onko vihdoin aika keskustella ehdoista ja rajoista? Tiedepolitiikka 45(4): 16-21. Linkki artikkeliin.
Datan esittäminen ja visualisointi
  • He, C., Raj, V., Moen, H., Gröhn, T., Wang, C., Peltonen, L. M., ... & Jacucci, G. (2024, March). VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. In Proceedings of the 29th International Conference on Intelligent User Interfaces (pp. 229-244). Linkki artikkelin
  • He, C., Welsch, R., & Jacucci, G. (2024). A Pilot Study Comparing ChatGPT and Google Search in Supporting Visualization Insight Discovery. In Workshops at the International Conference on Intelligent User Interfaces. CEUR-WS. org. Linkki artikkeliin
  • Rusanen, A-M. (2019). Pikseleitä, kohinaa ja haurautta. Niin & näin : filosofinen aikakauslehti, 26(3), 47-53. Linkki artikkeliin.
  • Rusanen, A-M. (2021). Algoritmien aakkoset. In book: Älykäs huominen - miten tekoäly ja digitalisaatio muuttavat maailmaa? (pp.33-39) Publisher: Gaudeamus. Linkki artikkeliin.
  • Rusanen, A-M., Lappi, O., Kuokkanen, J., & Pekkanen, J. (2021). Action control, forward models and expected rewards: representations in reinforcement learning. Synthese, (199), 14017–14033. Linkki artikkeliin.
Julkinen sektori datan hyödyntäjänä
  • Åm, H., Jensen, L. G., Hansen, R. M., Snell, K., Tarkkala, H., & Tupasela, A. (2025). The politics of constructing health data spaces: Border work and the stickiness of fragmentation. Big Data & Society, 2025. Linkki artikkeliin.
  • Helén, I., Snell, K., Tarkkala, H., & Tupasela, A. (2024). Genome Finland: From rare diseases to data economy (p. 310). Helsinki University Press. Linkki artikkeliin.
  • Iisakka, E. & Alastalo, M. (2024). Digitaalisen sosiaali- ja terveydenhuollon lupaukset: Kriittinen luenta hyvinvointialuestrategioiden sosioteknisestä mielikuvastosta. Sosiologia 61:3, 211–227. Linkki artikkeliin. 
  • Kytö, M., Hotta, S., Niinistö, S., Marttinen, P., Korhonen, T.E., Markussen, L.T., Jacucci, G., Sievänen, H., Vähä-Ypyä, H., Korhonen, I., Virtanen, S., Heinonen, S., and Koivusalo, S. (2024). Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial. American Journal of Obstetrics and Gynecology. Linkki artikkeliin.
  • Tervonen, L., & Alastalo, M. (2024). Visualisoinnit hallinnassa?: Tapaus TEAviisari suomalaisessa hyvinvointipolitiikassa. Linkki artikkeliin
  • Tarkkala Heta, Snell Karoliina: Sociala synpunkter på användningen av genetiska data inom vården och i samhället (2023). Linkki artikkeliin.
  • Alastalo, Marja & Järvinen, Katri-Maria 2023. Digitaalisen hyvinvointivaltion jäljillä: Haastateltavana Virginia Eubanks. Kulttuurintutkimus 40:2, 72–79. Linkki artikkeliin.
  • Choroszewicz, M. (2023). (In) visible everyday work of fostering a data‐driven healthcare and social service organisation. New Technology, Work and Employment. Linkki artikkeliin
  • Tupasela, A. (2022). The Genetic Imagination: Imaging Populations and the Construction of Nationhood. In J. Hoegaerts, T. Liimatainen, L. Hekanaho, & E. Peterson (Eds.), Finnishness, Whiteness and Coloniality (pp. 19-40). Helsinki University Press. Linkki artikkeliin. 
  • Choroszewicz, M. (2022) Emotional labour in the collaborative data practices of repurposing healthcare data and building data technologies. Big Data & Society 9(1), 1-12. Linkki artikkeliin.
  • Alastalo M., Parviainen J. & Choroszewicz M. (2022) Tekoälyteknologian kotoistaminen julkisiin palveluihin : Tapaus Espoon tekoälykokeilu. Yhteiskuntapolitiikka 87 (2022):3, 285-296. Linkki artikkeliin.
  • Alastalo M and Helén I. (2022) A code for care and control: The PIN as an operator of interoperability in the Nordic welfare state. History of the Human Sciences. 2022;35(1):242-265. Linkki artikkeliin.
  • Choroszewicz M. and Alastalo M. (2021) "Organisational and Professional Hierarchies in a Data Management System: Public–Private Collaborative Building of Public Healthcare and Social Services in Finland". Information, Communication & Society. Linkki artikkeliin.
  • Ustek-Spilda F. & Alastalo M. (2020) Software-Sorted Exclusion of Asylum Seekers in Norway and Finland. Global Perspectives 11 May 2020; 1 (1): 12978. Linkki artikkeliin.
  • Snell, K. (2020) Henkilökohtaisten terveystietojen hyödyntämisen oikeutukset: Onko vihdoin aika keskustella ehdoista ja rajoista? Tiedepolitiikka 45(4): 16-21. Linkki artikkeliin.

Uusimmat julkaisut

Alakuijala, M., McLean, R., Woungang, I., Farsad, N., Kaski, S., Marttinen, P., and Yuan, K. (2025). Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. Transactions on Machine Learning Research (TMLR).

Avaa

He, C., Welsch, R., & Jacucci, G. (2024). A Pilot Study Comparing ChatGPT and Google Search in Supporting Visualization Insight Discovery. In Workshops at the International Conference on Intelligent User Interfaces. CEUR-WS. org.

Avaa

He, C., Raj, V., Moen, H., Gröhn, T., Wang, C., Peltonen, L. M., ... & Jacucci, G. (2024). VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. In Proceedings of the 29th International Conference on Intelligent User Interfaces (pp. 229-244).

Avaa

Tervonen, L., & Alastalo, M. (2024). Visualisoinnit hallinnassa?: Tapaus TEAviisari suomalaisessa hyvinvointipolitiikassa.

Avaa

Iisakka, Essi & Alastalo, Marja 2024. Digitaalisen sosiaali- ja terveydenhuollon lupaukset: Kriittinen luenta hyvinvointialuestrategioiden sosioteknisestä mielikuvastosta. Sosiologia 61:3, 211–227.

Avaa