Juho Piironen
Juho Piironen
Senior scientist, Ph.D., Top Data Science company
Verified email at topdatascience.com - Homepage
Title
Cited by
Cited by
Year
Comparison of Bayesian predictive methods for model selection
J Piironen, A Vehtari
Statistics and Computing 27 (3), 711-735, 2017
2292017
Sparsity information and regularization in the horseshoe and other shrinkage priors
J Piironen, A Vehtari
Electronic Journal of Statistics 11 (2), 5018-5051, 2017
2072017
On the hyperprior choice for the global shrinkage parameter in the horseshoe prior
J Piironen, A Vehtari
Artificial Intelligence and Statistics, 905-913, 2017
682017
Projective inference in high-dimensional problems: Prediction and feature selection
J Piironen, M Paasiniemi, A Vehtari
Electronic Journal of Statistics 14 (1), 2155-2197, 2020
352020
Projection predictive model selection for Gaussian processes
J Piironen, A Vehtari
2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016
302016
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models.(2016)
A Vehtari, A Gelman, J Gabry, Y Yao, PC Bürkner, B Goodrich, J Piironen, ...
URL https://github. com/stan-dev/loo. R package version 1 (0), 6, 0
22
Projection predictive variable selection using Stan+ R
J Piironen, A Vehtari
arXiv preprint arXiv:1508.02502, 2015
192015
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
142019
Iterative supervised principal components
J Piironen, A Vehtari
International Conference on Artificial Intelligence and Statistics, 106-114, 2018
142018
Implicitly adaptive importance sampling
T Paananen, J Piironen, PC Bürkner, A Vehtari
Statistics and Computing 31 (2), 1-19, 2021
112021
Bayesian estimation of Gaussian graphical models with predictive covariance selection
DR Williams, J Piironen, A Vehtari, P Rast
arXiv preprint arXiv:1801.05725, 2018
82018
Using reference models in variable selection
F Pavone, J Piironen, PC Bürkner, A Vehtari
arXiv preprint arXiv:2004.13118, 2020
52020
projpred: Projection Predictive Feature Selection.(2019)
J Piironen, M Paasiniemi, A Vehtari, J Gabry, PC Bürkner
5
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
42020
Bayesian estimation of Gaussian graphical models with projection predictive selection
DR Williams, J Piironen, A Vehtari, P Rast
ArXiv e-prints, 2018
42018
Automatic monotonicity detection for Gaussian Processes
E Siivola, J Piironen, A Vehtari
arXiv preprint arXiv:1610.05440, 2016
42016
Making Bayesian predictive models interpretable: a decision theoretic approach
H Afrabandpey, T Peltola, J Piironen, A Vehtari, S Kaski
arXiv, 1910
41910
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
32018
Contributed comment on article by Van der Pas, Szabo, and Van der Vaart
J Piironen, M Betancourt, D Simpson, A Vehtari
Bayesian Analysis 12 (4), 1264-1266, 2017
32017
Projection predictive input variable selection for Gaussian process models
J Piironen, A Vehtari
arXiv preprint arXiv:1510.04813, 2015
22015
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