Citations to my work can be found via Google Scholar.

Peer-reviewed articles

Federico Pavone, Juho Piironen, Paul-Christian Bürkner and Aki Vehtari (2022). Using reference models in variable selection. Computational Statistics. Online Preprint
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Antti Piironen, Juho Piironen and Toni Laaksonen (2022). Predicting spatio-temporal distributions of migratory populations using Gaussian process modelling. Journal of Applied Ecology, 00: 1-11. (First two authors contributed equally) Online Codes
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Topi Paananen, Juho Piironen, Paul Bürkner and Aki Vehtari (2020). Implicitly Adaptive Importance Sampling. Statistics and Computing, 31:16. Online Preprint
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Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, and Samuel Kaski (2020). A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework. Machine Learning, 109:1855-1876. Special Issue of the ECML PKDD 2020 Journal Track. Online Preprint
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Juho Piironen, Markus Paasiniemi and Aki Vehtari (2020). Projective Inference in High-dimensional Problems: Prediction and Feature Selection. Electronic Journal of Statistics, 14(1): 2155-2197. Online Code
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Topi Paananen, Juho Piironen, Michael Riis Andersen and Aki Vehtari (2019). Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 89: 1743-1752. Online
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Simon Holmbacka, Jarno Niemelä, Henri Karikallio, Karri Sunila, Ilkka Raiskinen, Eero Siivola, Juho Piironen, Tuomas Sivula (2018). Alarm Prediction in LTE Networks. In Proceedings of the 2018 IEEE 25th International Conference on Telecommunications (ICT). Online
Juho Piironen and Aki Vehtari (2018). Iterative Supervised Principal Components. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84: 106-114. Online Poster Code
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Juho Piironen and Aki Vehtari (2017). Sparsity information and regularization in the horseshoe and other shrinkage priors. Electronic Journal of Statistics, 11(2): 5018-5051. Online
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Juho Piironen and Aki Vehtari (2017). On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54: 905-913. Online Talk Poster
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Juho Piironen and Aki Vehtari (2017). Comparison of Bayesian predictive methods for model selection. Statistics and Computing, 27(3): 711-735. Online
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Juho Piironen and Aki Vehtari (2016). Projection predictive model selection for Gaussian processes. In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). Online Preprint
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Technical reports and other scientific articles

  • Donald R. Williams, Juho Piironen, Aki Vehtari and Philippe Rast (2018). Bayesian Estimation of Gaussian Graphical Models with Projection Predictive Selection. Online

  • Juho Piironen, Michael Betancourt, Daniel Simpson and Aki Vehtari (2017). Discussion to Uncertainty Quantification for the Horseshoe by Stéphanie van der Pas, Botond Szabó and Aad van der Vaart. Bayesian Analysis. Online

  • Eero Siivola, Juho Piironen and Aki Vehtari (2016). Automatic monotonicity detection for Gaussian Processes. Online

  • Juho Piironen and Aki Vehtari (2015). Projection predictive variable selection using Stan+R. Online

Thesis

Juho Piironen (2019). Bayesian Predictive Inference and Feature Selection for High-Dimensional Data. Doctoral dissertation, Aalto University. Online Lectio praecursoria (in Finnish)
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⭐ The thesis won two awards:

Best doctoral thesis of year 2019 in the field of computer science, awarded by the Finnish Society for Computer Science (Tietotekniikan tutkimussäätiö).

Among the top 10% of doctoral dissertations in Aalto University School of Science in 2019, judged by their academic quality, originality, and impact.