Aki Vehtari

According to our database, Aki Vehtari authored at least 91 papers between 1999 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Efficient estimation and correction of selection-induced bias with order statistics.

Yann McLatchie, Aki Vehtari

Stat. Comput., August, 2024

Bayesian cross-validation by parallel Markov chain Monte Carlo.

Alex Cooper, Aki Vehtari, Catherine Forbes, Daniel Simpson, Lauren Kennedy

Stat. Comput., August, 2024

Detecting and diagnosing prior and likelihood sensitivity with power-scaling.

Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari

Stat. Comput., February, 2024

A Framework for Improving the Reliability of Black-box Variational Inference.

Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

J. Mach. Learn. Res., 2024

Pareto Smoothed Importance Sampling.

Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

J. Mach. Learn. Res., 2024

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models.

Ryoko Noda, Michael Mechenich, Juha Saarinen, Aki Vehtari, Indre Zliobaite

Ecol. Informatics, 2024

Amortized Bayesian Workflow (Extended Abstract).

Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev

CoRR, 2024

Active Learning of Molecular Data for Task-Specific Objectives.

Kunal Ghosh, Milica Todorovic, Aki Vehtari, Patrick Rinke

CoRR, 2024

2023

Using reference models in variable selection.

Federico Pavone, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari

Comput. Stat., March, 2023

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming.

Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael Riis Andersen, Arno Solin, Aki Vehtari

Stat. Comput., 2023

2022

Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison.

Teemu Säilynoja, Paul-Christian Bürkner, Aki Vehtari

Stat. Comput., 2022

Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort.

Tuulia Malén, Tomi Karjalainen, Janne Isojärvi, Aki Vehtari, Paul-Christian Bürkner, Vesa Putkinen, Valtteri Kaasinen, Jarmo Hietala, Pirjo Nuutila, Juha O. Rinne, Lauri Nummenmaa

NeuroImage, 2022

Pathfinder: Parallel quasi-Newton variational inference.

Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari

J. Mach. Learn. Res., 2022

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors.

Yuling Yao, Aki Vehtari, Andrew Gelman

J. Mach. Learn. Res., 2022

A fast regression via SVD and marginalization.

Philip Greengard, Andrew Gelman, Aki Vehtari

Comput. Stat., 2022

Robust, Automated, and Accurate Black-box Variational Inference.

Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

CoRR, 2022

Feature Collapsing for Gaussian Process Variable Ranking.

Isaac Sebenius, Topi Paananen, Aki Vehtari

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models.

Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Implicitly adaptive importance sampling.

Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari

Stat. Comput., 2021

Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models.

Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari

Comput. Stat., 2021

Bayesian hierarchical stacking.

Yuling Yao, Gregor Pirs, Aki Vehtari, Andrew Gelman

CoRR, 2021

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data.

Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki

Bioinform., 2021

Uncertainty-aware sensitivity analysis using Rényi divergences.

Topi Paananen, Michael Riis Andersen, Aki Vehtari

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Challenges and Opportunities in High Dimensional Variational Inference.

Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Preferential Batch Bayesian Optimization.

Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari

Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

2020

Interindividual variability and lateralization of μ-opioid receptors in the human brain.

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

NeuroImage, 2020

A decision-theoretic approach for model interpretability in Bayesian framework.

Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski

Mach. Learn., 2020

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data.

Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert

J. Mach. Learn. Res., 2020

Good practices for Bayesian Optimization of high dimensional structured spaces.

Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari

CoRR, 2020

A Fast Linear Regression via SVD and Marginalization.

Philip Greengard, Andrew Gelman, Aki Vehtari

CoRR, 2020

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation.

Marko Järvenpää, Aki Vehtari, Pekka Marttinen

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond.

Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust, Accurate Stochastic Optimization for Variational Inference.

Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Scalable Gaussian Process for Extreme Classification.

Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data.

Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies.

Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki

CoRR, 2019

Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach.

Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski

CoRR, 2019

Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation.

Marko Järvenpää, Aki Vehtari, Pekka Marttinen

CoRR, 2019

Parallel Gaussian process surrogate method to accelerate likelihood-free inference.

Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen

CoRR, 2019

Active Learning for Decision-Making from Imbalanced Observational Data.

Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski

Proceedings of the 36th International Conference on Machine Learning, 2019

Bayesian leave-one-out cross-validation for large data.

Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari

Proceedings of the 36th International Conference on Machine Learning, 2019

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.

Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

ELFI: Engine for Likelihood-Free Inference.

Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski

J. Mach. Learn. Res., 2018

Projective Inference in High-dimensional Problems: Prediction and Feature Selection.

Juho Piironen, Markus Paasiniemi, Aki Vehtari

CoRR, 2018

Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations.

Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen

Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction.

Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski

Proceedings of the 23rd International Conference on Intelligent User Interfaces, 2018

Yes, but Did It Work?: Evaluating Variational Inference.

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

Proceedings of the 35th International Conference on Machine Learning, 2018

Iterative Supervised Principal Components.

Juho Piironen, Aki Vehtari

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.

Aki Vehtari, Andrew Gelman, Jonah Gabry

Stat. Comput., 2017

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.

Aki Vehtari, Andrew Gelman, Jonah Gabry

Stat. Comput., 2017

Comparison of Bayesian predictive methods for model selection.

Juho Piironen, Aki Vehtari

Stat. Comput., 2017

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

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

NeuroImage, 2017

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors.

Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen

J. Mach. Learn. Res., 2017

ELFI: Engine for Likelihood Free Inference.

Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski

CoRR, 2017

On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior.

Juho Piironen, Aki Vehtari

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models.

Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther

J. Mach. Learn. Res., 2016

Automatic detection of acute kidney injury episodes from primary care data.

Santosh Tirunagari, Simon C. Bull, Aki Vehtari, Christopher Farmer, Simon de Lusignan, Norman Poh

Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Projection predictive model selection for Gaussian processes.

Juho Piironen, Aki Vehtari

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

Chained Gaussian Processes.

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2014

Understanding predictive information criteria for Bayesian models.

Andrew Gelman, Jessica Hwang, Aki Vehtari

Stat. Comput., 2014

Expectation propagation for neural networks with sparsity-promoting priors.

Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari

J. Mach. Learn. Res., 2014

Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction.

Tomi Peltola, Aki S. Havulinna, Veikko Salomaa, Aki Vehtari

Proceedings of the Eleventh UAI Bayesian Modeling Applications Workshop co-located with the 30th Conference on Uncertainty in Artificial Intelligence, 2014

Expectation propagation for nonstationary heteroscedastic Gaussian process regression.

Ville Tolvanen, Pasi Jylänki, Aki Vehtari

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables.

Tomi Peltola, Pasi Jylänki, Aki Vehtari

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

GPstuff: Bayesian modeling with Gaussian processes.

Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari

J. Mach. Learn. Res., 2013

Nested expectation propagation for Gaussian process classification.

Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari

J. Mach. Learn. Res., 2013

2012

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER.

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, Fa-Hsuan Lin

NeuroImage, 2012

Bayesian Modeling with Gaussian Processes using the MATLAB Toolbox GPstuff (v3.3)

Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari

CoRR, 2012

2011

Robust Gaussian Process Regression with a Student-<i>t</i> Likelihood.

Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari

J. Mach. Learn. Res., 2011

2010

Gaussian processes with monotonicity information.

Jaakko Riihimäki, Aki Vehtari

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Speeding up the binary Gaussian process classification.

Jarno Vanhatalo, Aki Vehtari

Proceedings of the UAI 2010, 2010

Explaining Classification by Finding Response-Related Subgroups in Data.

Elina Parviainen, Aki Vehtari

Proceedings of the 11th ACIS International Conference on Software Engineering, 2010

2009

Gaussian process regression with Student-t likelihood.

Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Features and Metric from a Classifier Improve Visualizations with Dimension Reduction.

Elina Parviainen, Aki Vehtari

Proceedings of the Artificial Neural Networks, 2009

2008

Modelling local and global phenomena with sparse Gaussian processes.

Jarno Vanhatalo, Aki Vehtari

Proceedings of the UAI 2008, 2008

2007

Automatic relevance determination based hierarchical Bayesian MEG inversion in practice.

Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Mikko Sams, Aki Vehtari, Jouko Lampinen

NeuroImage, 2007

Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods.

Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Mikko Sams, Aki Vehtari

NeuroImage, 2007

Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology.

Jarno Vanhatalo, Aki Vehtari

Proceedings of the Gaussian Processes in Practice, 2007

Rao-Blackwellized particle filter for multiple target tracking.

Simo Särkkä, Aki Vehtari, Jouko Lampinen

Inf. Fusion, 2007

CATS benchmark time series prediction by Kalman smoother with cross-validated noise density.

Simo Särkkä, Aki Vehtari, Jouko Lampinen

Neurocomputing, 2007

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in ¹H NMR metabonomic data.

Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski, Mika Ala-Korpela

BMC Bioinform., 2007

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles.

Marko Sysi-Aho, Aki Vehtari, Vidya R. Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen, Matej Oresic

Proceedings of the Proceedings 15th International Conference on Intelligent Systems for Molecular Biology (ISMB) & 6th European Conference on Computational Biology (ECCB), 2007

2005

Bayesian analysis of the neuromagnetic inverse problem with ℓ^p-norm priors.

Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, Mikko Sams

NeuroImage, 2005

Shape analysis of concrete aggregates for statistical quality modeling.

Ilkka Kalliomäki, Aki Vehtari, Jouko Lampinen

Mach. Vis. Appl., 2005

2002

Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities.

Aki Vehtari, Jouko Lampinen

Neural Comput., 2002

2001

Bayesian approach for neural networks--review and case studies.

Jouko Lampinen, Aki Vehtari

Neural Networks, 2001

2000

Bayesian MLP neural networks for image analysis.

Aki Vehtari, Jouko Lampinen

Pattern Recognit. Lett., 2000

On MCMC Sampling in Bayesian MLP Neural Networks.

Aki Vehtari, Simo Särkkä, Jouko Lampinen

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Bayesian techniques for neural networks - Review and case studies.

Jouko Lampinen, Aki Vehtari

Proceedings of the 10th European Signal Processing Conference, 2000

1999

Bayesian neural networks with correlating residuals.

Aki Vehtari, Jouko Lampinen

Proceedings of the International Joint Conference Neural Networks, 1999

Application of Bayesian neural network in electrical impedance tomography.

Jouko Lampinen, Aki Vehtari, Kimmo Leinonen

Proceedings of the International Joint Conference Neural Networks, 1999