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Juho Piironen
Juho Piironen
Lead scientist, Ph.D., Top Data Science company
Verified email at topdatascience.com - Homepage
Title
Cited by
Cited by
Year
Sparsity information and regularization in the horseshoe and other shrinkage priors
J Piironen, A Vehtari
4132017
Comparison of Bayesian predictive methods for model selection
J Piironen, A Vehtari
Statistics and Computing 27, 711-735, 2017
3532017
On the hyperprior choice for the global shrinkage parameter in the horseshoe prior
J Piironen, A Vehtari
Artificial intelligence and statistics, 905-913, 2017
1182017
Projective inference in high-dimensional problems: Prediction and feature selection
J Piironen, M Paasiniemi, A Vehtari
1102020
Projection predictive model selection for Gaussian processes
J Piironen, A Vehtari
2016 IEEE 26th international workshop on machine learning for signal …, 2016
532016
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
T Paananen, J Piironen, MR Andersen, A Vehtari
The 22nd international conference on artificial intelligence and statistics …, 2019
512019
Implicitly adaptive importance sampling
T Paananen, J Piironen, PC Bürkner, A Vehtari
Statistics and Computing 31 (2), 16, 2021
342021
Projection predictive variable selection using Stan+ R
J Piironen, A Vehtari
arXiv preprint arXiv:1508.02502, 2015
252015
Using reference models in variable selection
F Pavone, J Piironen, PC Bürkner, A Vehtari
Computational Statistics 38 (1), 349-371, 2023
182023
Iterative supervised principal components
J Piironen, A Vehtari
International Conference on Artificial Intelligence and Statistics, 106-114, 2018
182018
Projpred: projection predictive feature selection
J Piironen, M Paasiniemi, A Catalina, A Vehtari
R package version 2, 22020, 2018
152018
A decision-theoretic approach for model interpretability in Bayesian framework
H Afrabandpey, T Peltola, J Piironen, A Vehtari, S Kaski
Machine learning 109 (9), 1855-1876, 2020
132020
Bayesian estimation of Gaussian graphical models with predictive covariance selection
DR Williams, J Piironen, A Vehtari, P Rast
arXiv preprint arXiv:1801.05725, 2018
102018
Bayesian estimation of Gaussian graphical models with projection predictive selection
DR Williams, J Piironen, A Vehtari, P Rast
arXiv preprint arXiv:1801.05725, 2018
72018
Automatic monotonicity detection for gaussian processes
E Siivola, J Piironen, A Vehtari
arXiv preprint arXiv:1610.05440, 2016
62016
Alarm prediction in LTE networks
S Holmbacka, J Niemelä, H Karikallio, K Sunila, I Raiskinen, E Siivola, ...
2018 25th International Conference on Telecommunications (ICT), 341-345, 2018
52018
Predicting spatio‐temporal distributions of migratory populations using Gaussian process modelling
A Piironen, J Piironen, T Laaksonen
Journal of Applied Ecology 59 (4), 1146-1156, 2022
42022
Making Bayesian predictive models interpretable: A decision theoretic approach
H Afrabandpey, T Peltola, J Piironen, A Vehtari, S Kaski
arXiv: 1910.09358, 2019
42019
Contributed comment on article by van der Pas, Szabó, and van der Vaart
J Piironen, M Betancourt, D Simpson, A Vehtari
Bayesian Analysis 12 (4), 1264-1266, 2017
42017
projpred: Projection Predictive Feature Selection.(2019)
J Piironen, M Paasiniemi, A Vehtari, J Gabry, PC Bürkner
3
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