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2020 – today
- 2024
[j42]Ryoko Noda, Michael Mechenich
, Juha Saarinen, Aki Vehtari, Indre Zliobaite:
Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. Ecol. Informatics 82: 102658 (2024)- [j41]Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry:
Pareto Smoothed Importance Sampling. J. Mach. Learn. Res. 25: 72:1-72:58 (2024) - [j40]Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. 25: 219:1-219:71 (2024) - [j39]Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari:
Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Stat. Comput. 34(1): 57 (2024) - [j38]Alex Cooper, Aki Vehtari, Catherine Forbes, Daniel Simpson, Lauren Kennedy:
Bayesian cross-validation by parallel Markov chain Monte Carlo. Stat. Comput. 34(4): 119 (2024) - [j37]Yann McLatchie, Aki Vehtari:
Efficient estimation and correction of selection-induced bias with order statistics. Stat. Comput. 34(4): 132 (2024) - [i23]Kunal Ghosh, Milica Todorovic, Aki Vehtari, Patrick Rinke:
Active Learning of Molecular Data for Task-Specific Objectives. CoRR abs/2408.11191 (2024) - [i22]Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev:
Amortized Bayesian Workflow (Extended Abstract). CoRR abs/2409.04332 (2024) - 2023
- [j36]Federico Pavone, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari:
Using reference models in variable selection. Comput. Stat. 38(1): 349-371 (2023) - [j35]Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael Riis Andersen, Arno Solin, Aki Vehtari:
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Stat. Comput. 33(1): 17 (2023) - 2022
- [j34]Philip Greengard, Andrew Gelman, Aki Vehtari:
A fast regression via SVD and marginalization. Comput. Stat. 37(2): 701-720 (2022) - [j33]Yuling Yao, Aki Vehtari, Andrew Gelman:
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. J. Mach. Learn. Res. 23: 79:1-79:45 (2022) - [j32]Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari:
Pathfinder: Parallel quasi-Newton variational inference. J. Mach. Learn. Res. 23: 306:1-306:49 (2022) - [j31]Tuulia Malén, Tomi Karjalainen, Janne Isojärvi, Aki Vehtari, Paul-Christian Bürkner, Vesa Putkinen, Valtteri Kaasinen, Jarmo Hietala, Pirjo Nuutila, Juha O. Rinne, Lauri Nummenmaa:
Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort. NeuroImage 255: 119149 (2022) - [j30]Teemu Säilynoja, Paul-Christian Bürkner, Aki Vehtari:
Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. Stat. Comput. 32(2): 32 (2022) - [c36]Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari:
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. AISTATS 2022: 4446-4461 - [c35]Isaac Sebenius, Topi Paananen, Aki Vehtari:
Feature Collapsing for Gaussian Process Variable Ranking. AISTATS 2022: 11341-11355 - [i21]Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
Robust, Automated, and Accurate Black-box Variational Inference. CoRR abs/2203.15945 (2022) - 2021
- [j29]Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki:
lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinform. 37(13): 1860-1867 (2021) - [j28]Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari:
Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models. Comput. Stat. 36(2): 1243-1261 (2021) - [j27]Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari:
Implicitly adaptive importance sampling. Stat. Comput. 31(2): 16 (2021) - [c34]Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari:
Preferential Batch Bayesian Optimization. MLSP 2021: 1-6 - [c33]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS 2021: 7787-7798 - [c32]Topi Paananen, Michael Riis Andersen, Aki Vehtari:
Uncertainty-aware sensitivity analysis using Rényi divergences. UAI 2021: 1185-1194 - [i20]Yuling Yao, Gregor Pirs, Aki Vehtari, Andrew Gelman:
Bayesian hierarchical stacking. CoRR abs/2101.08954 (2021) - [i19]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High-dimensional Variational Inference. CoRR abs/2103.01085 (2021) - [i18]Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari:
Pathfinder: Parallel quasi-Newton variational inference. CoRR abs/2108.03782 (2021) - 2020
- [j26]Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert:
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. 21: 17:1-17:53 (2020) - [j25]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) - [j24]Tatu Kantonen, Tomi Karjalainen, Janne Isojärvi, Pirjo Nuutila, Jouni Tuisku, Juha O. Rinne, Jarmo Hietala, Valtteri Kaasinen, Kari Kalliokoski, Harry Scheinin, Jussi Hirvonen, Aki Vehtari, Lauri Nummenmaa:
Interindividual variability and lateralization of μ-opioid receptors in the human brain. NeuroImage 217: 116922 (2020) - [c31]Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen:
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. AISTATS 2020: 341-351 - [c30]Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari:
Scalable Gaussian Process for Extreme Classification. MLSP 2020: 1-6 - [c29]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS 2020 - [c28]Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal:
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. NeurIPS 2020 - [c27]Marko Järvenpää, Aki Vehtari, Pekka Marttinen:
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. UAI 2020: 779-788 - [i17]Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari:
Preferential Batch Bayesian Optimization. CoRR abs/2003.11435 (2020) - [i16]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR abs/2009.00666 (2020) - [i15]Philip Greengard, Andrew Gelman, Aki Vehtari:
A Fast Linear Regression via SVD and Marginalization. CoRR abs/2011.04829 (2020) - [i14]Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari:
Good practices for Bayesian Optimization of high dimensional structured spaces. CoRR abs/2012.15471 (2020)
2010 – 2019
- 2019
- [c26]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 - [c25]Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari:
Bayesian leave-one-out cross-validation for large data. ICML 2019: 4244-4253 - [c24]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. ICML 2019: 6046-6055 - [i13]Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. CoRR abs/1904.05268 (2019) - [i12]Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari:
Bayesian leave-one-out cross-validation for large data. CoRR abs/1904.10679 (2019) - [i11]Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen:
Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR abs/1905.01252 (2019) - [i10]Marko Järvenpää, Aki Vehtari, Pekka Marttinen:
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. CoRR abs/1910.06121 (2019) - [i9]Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski:
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. CoRR abs/1910.09358 (2019) - [i8]Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki:
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. CoRR abs/1912.03549 (2019) - 2018
- [j23]Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. 19: 16:1-16:7 (2018) - [c23]Juho Piironen, Aki Vehtari:
Iterative Supervised Principal Components. AISTATS 2018: 106-114 - [c22]Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman:
Yes, but Did It Work?: Evaluating Variational Inference. ICML 2018: 5577-5586 - [c21]Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski:
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. IUI 2018: 305-310 - [c20]Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen:
Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. MLSP 2018: 1-6 - [i7]Juho Piironen, Markus Paasiniemi, Aki Vehtari:
Projective Inference in High-dimensional Problems: Prediction and Feature Selection. CoRR abs/1810.02406 (2018) - 2017
- [j22]Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen:
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. 18: 139:1-139:58 (2017) - [j21]Juha Salmi, Olli-Pekka Koistinen, Enrico Glerean, Pasi Jylänki, Aki Vehtari, Iiro P. Jääskeläinen, Sasu Mäkelä, Lauri Nummenmaa, Katarina Nummi-Kuisma, Ilari Nummi, Mikko Sams:
Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex. NeuroImage 157: 108-117 (2017) - [j20]Juho Piironen, Aki Vehtari:
Comparison of Bayesian predictive methods for model selection. Stat. Comput. 27(3): 711-735 (2017) - [j19]Aki Vehtari, Andrew Gelman, Jonah Gabry:
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5): 1413-1432 (2017) - [j18]Aki Vehtari, Andrew Gelman, Jonah Gabry:
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5): 1433 (2017) - [c19]Juho Piironen, Aki Vehtari:
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. AISTATS 2017: 905-913 - [i6]Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood Free Inference. CoRR abs/1708.00707 (2017) - [i5]Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski:
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. CoRR abs/1710.04881 (2017) - 2016
- [j17]Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther:
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models. J. Mach. Learn. Res. 17: 103:1-103:38 (2016) - [c18]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. AISTATS 2016: 1431-1440 - [c17]Juho Piironen, Aki Vehtari:
Projection predictive model selection for Gaussian processes. MLSP 2016: 1-6 - [c16]Santosh Tirunagari, Simon C. Bull, Aki Vehtari, Christopher Farmer, Simon de Lusignan, Norman Poh:
Automatic detection of acute kidney injury episodes from primary care data. SSCI 2016: 1-6 - [i4]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. CoRR abs/1604.05263 (2016) - 2014
- [j16]Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari:
Expectation propagation for neural networks with sparsity-promoting priors. J. Mach. Learn. Res. 15(1): 1849-1901 (2014) - [j15]Andrew Gelman, Jessica Hwang, Aki Vehtari:
Understanding predictive information criteria for Bayesian models. Stat. Comput. 24(6): 997-1016 (2014) - [c15]Tomi Peltola, Pasi Jylänki, Aki Vehtari:
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. AISTATS 2014: 769-777 - [c14]Ville Tolvanen, Pasi Jylänki, Aki Vehtari:
Expectation propagation for nonstationary heteroscedastic Gaussian process regression. MLSP 2014: 1-6 - [c13]Tomi Peltola, Aki S. Havulinna, Veikko Salomaa, Aki Vehtari:
Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction. BMA@UAI 2014: 79-88 - 2013
- [j14]Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari:
Nested expectation propagation for Gaussian process classification. J. Mach. Learn. Res. 14(1): 75-109 (2013) - [j13]Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari:
GPstuff: Bayesian modeling with Gaussian processes. J. Mach. Learn. Res. 14(1): 1175-1179 (2013) - 2012
- [j12]Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, Fa-Hsuan Lin:
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage 60(2): 1517-1527 (2012) - [i3]Jarno Vanhatalo, Aki Vehtari:
Speeding up the binary Gaussian process classification. CoRR abs/1203.3524 (2012) - [i2]Jarno Vanhatalo, Aki Vehtari:
Modelling local and global phenomena with sparse Gaussian processes. CoRR abs/1206.3290 (2012) - [i1]Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari:
Bayesian Modeling with Gaussian Processes using the MATLAB Toolbox GPstuff (v3.3). CoRR abs/1206.5754 (2012) - 2011
- [j11]Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari:
Robust Gaussian Process Regression with a Student-t Likelihood. J. Mach. Learn. Res. 12: 3227-3257 (2011) - 2010
- [c12]Elina Parviainen, Aki Vehtari:
Explaining Classification by Finding Response-Related Subgroups in Data. SNPD 2010: 69-75 - [c11]Jarno Vanhatalo, Aki Vehtari:
Speeding up the binary Gaussian process classification. UAI 2010: 623-631 - [c10]Jaakko Riihimäki, Aki Vehtari:
Gaussian processes with monotonicity information. AISTATS 2010: 645-652
2000 – 2009
- 2009
- [c9]Elina Parviainen, Aki Vehtari:
Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. ICANN (2) 2009: 225-234 - [c8]Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari:
Gaussian process regression with Student-t likelihood. NIPS 2009: 1910-1918 - 2008
- [c7]Jarno Vanhatalo, Aki Vehtari:
Modelling local and global phenomena with sparse Gaussian processes. UAI 2008: 571-578 - 2007
- [j10]Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski, Mika Ala-Korpela:
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data. BMC Bioinform. 8(S-2) (2007) - [j9]Simo Särkkä, Aki Vehtari, Jouko Lampinen:
CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing 70(13-15): 2331-2341 (2007) - [j8]Simo Särkkä, Aki Vehtari, Jouko Lampinen:
Rao-Blackwellized particle filter for multiple target tracking. Inf. Fusion 8(1): 2-15 (2007) - [j7]Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Mikko Sams, Aki Vehtari:
Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods. NeuroImage 35(2): 669-685 (2007) - [j6]Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Mikko Sams, Aki Vehtari, Jouko Lampinen:
Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. NeuroImage 37(3): 876-889 (2007) - [c6]Marko Sysi-Aho, Aki Vehtari, Vidya R. Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen, Matej Oresic:
Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. ISMB/ECCB (Supplement of Bioinformatics) 2007: 519-528 - [c5]Jarno Vanhatalo, Aki Vehtari:
Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology. Gaussian Processes in Practice 2007: 73-89 - 2005
- [j5]Ilkka Kalliomäki, Aki Vehtari, Jouko Lampinen:
Shape analysis of concrete aggregates for statistical quality modeling. Mach. Vis. Appl. 16(3): 197-201 (2005) - [j4]Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, Mikko Sams:
Bayesian analysis of the neuromagnetic inverse problem with ℓp-norm priors. NeuroImage 26(3): 870-884 (2005) - 2002
- [j3]Aki Vehtari, Jouko Lampinen:
Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities. Neural Comput. 14(10): 2439-2468 (2002) - 2001
- [j2]Jouko Lampinen, Aki Vehtari:
Bayesian approach for neural networks--review and case studies. Neural Networks 14(3): 257-274 (2001) - 2000
- [j1]Aki Vehtari, Jouko Lampinen:
Bayesian MLP neural networks for image analysis. Pattern Recognit. Lett. 21(13-14): 1183-1191 (2000) - [c4]Jouko Lampinen, Aki Vehtari:
Bayesian techniques for neural networks - Review and case studies. EUSIPCO 2000: 1-8 - [c3]Aki Vehtari, Simo Särkkä, Jouko Lampinen:
On MCMC Sampling in Bayesian MLP Neural Networks. IJCNN (1) 2000: 317-322
1990 – 1999
- 1999
- [c2]Aki Vehtari, Jouko Lampinen:
Bayesian neural networks with correlating residuals. IJCNN 1999: 1662-1665 - [c1]Jouko Lampinen, Aki Vehtari, Kimmo Leinonen:
Application of Bayesian neural network in electrical impedance tomography. IJCNN 1999: 3942-3947
Coauthor Index
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