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2020 – today
- 2023
[j4]Federico Pavone
, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari:
Using reference models in variable selection. Comput. Stat. 38(1): 349-371 (2023)- 2021
- [j3]Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari:
Implicitly adaptive importance sampling. Stat. Comput. 31(2): 16 (2021) - 2020
- [j2]Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski:
A decision-theoretic approach for model interpretability in Bayesian framework. Mach. Learn. 109(9-10): 1855-1876 (2020)
2010 – 2019
- 2019
- [b1]Juho Piironen:
Bayesian Predictive Inference and Feature Selection for High-Dimensional Data; (Bayesilainen ennustava päättely ja piirrevalinta korkeaulotteisille aineistoille). Aalto University, Espoo, Finland, 2019 - [c5]Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari:
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. AISTATS 2019: 1743-1752 - [i2]Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski:
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. CoRR abs/1910.09358 (2019) - 2018
- [c4]Juho Piironen, Aki Vehtari:
Iterative Supervised Principal Components. AISTATS 2018: 106-114 - [c3]Simon Holmbacka, Jarno Niemelä, Henri Karikallio, Karri Sunila, Ilkka Raiskinen, Eero Siivola, Juho Piironen, Tuomas Sivula:
Alarm Prediction in LTE Networks. ICT 2018: 341-345 - [i1]Juho Piironen, Markus Paasiniemi, Aki Vehtari:
Projective Inference in High-dimensional Problems: Prediction and Feature Selection. CoRR abs/1810.02406 (2018) - 2017
- [j1]Juho Piironen, Aki Vehtari:
Comparison of Bayesian predictive methods for model selection. Stat. Comput. 27(3): 711-735 (2017) - [c2]Juho Piironen, Aki Vehtari:
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. AISTATS 2017: 905-913 - 2016
- [c1]Juho Piironen, Aki Vehtari:
Projection predictive model selection for Gaussian processes. MLSP 2016: 1-6
Coauthor Index
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