Aki Vehtari

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 63

Top Topics

Gaussian Process

Variable Selection

Expectation Propagation

Top Venues

J. Mach. Learn. Res.

Publications

Yann McLatchie, Aki Vehtari
Efficient estimation and correction of selection-induced bias with order statistics. Stat. Comput. 34 (4) (2024)
Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari
Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Stat. Comput. 34 (1) (2024)
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 (2024)
Alex Cooper, Aki Vehtari, Catherine Forbes, Daniel Simpson, Lauren Kennedy
Bayesian cross-validation by parallel Markov chain Monte Carlo. Stat. Comput. 34 (4) (2024)
Federico Pavone, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari
Using reference models in variable selection. Comput. Stat. 38 (1) (2023)
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) (2023)
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
Robust, Automated, and Accurate Black-box Variational Inference. CoRR (2022)
Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. AISTATS (2022)
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 (2022)
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) (2022)
Yuling Yao, Aki Vehtari, Andrew Gelman
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. J. Mach. Learn. Res. 23 (2022)
Philip Greengard, Andrew Gelman, Aki Vehtari
A fast regression via SVD and marginalization. Comput. Stat. 37 (2) (2022)
Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
Pathfinder: Parallel quasi-Newton variational inference. J. Mach. Learn. Res. 23 (2022)
Isaac Sebenius, Topi Paananen, Aki Vehtari
Feature Collapsing for Gaussian Process Variable Ranking. AISTATS (2022)
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
Preferential Batch Bayesian Optimization. MLSP (2021)
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
Challenges and Opportunities in High-dimensional Variational Inference. CoRR (2021)
Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
Pathfinder: Parallel quasi-Newton variational inference. CoRR (2021)
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)
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) (2021)
Yuling Yao, Gregor Pirs, Aki Vehtari, Andrew Gelman
Bayesian hierarchical stacking. CoRR (2021)
Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari
Implicitly adaptive importance sampling. Stat. Comput. 31 (2) (2021)
Topi Paananen, Michael Riis Andersen, Aki Vehtari
Uncertainty-aware sensitivity analysis using Rényi divergences. UAI (2021)
Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinform. 37 (13) (2021)
Marko Järvenpää, Aki Vehtari, Pekka Marttinen
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. UAI (2020)
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) (2020)
Eero Siivola, Javier Gonzalez, Andrei Paleyes, Aki Vehtari
Good practices for Bayesian Optimization of high dimensional structured spaces. CoRR (2020)
Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. AISTATS (2020)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR (2020)
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 (2020)
Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari
Scalable Gaussian Process for Extreme Classification. MLSP (2020)
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier Gonzalez, Pablo Garcia Moreno, Aki Vehtari
Preferential Batch Bayesian Optimization. CoRR (2020)
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 (2020)
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)
Philip Greengard, Andrew Gelman, Aki Vehtari
A Fast Linear Regression via SVD and Marginalization. CoRR (2020)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS (2020)
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
Active Learning for Decision-Making from Imbalanced Observational Data. ICML (2019)
Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen
Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR (2019)
Marko Järvenpää, Aki Vehtari, Pekka Marttinen
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. CoRR (2019)
Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. CoRR (2019)
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
Bayesian leave-one-out cross-validation for large data. ICML (2019)
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
Bayesian leave-one-out cross-validation for large data. CoRR (2019)
Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. AISTATS (2019)
Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. CoRR (2019)
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
Active Learning for Decision-Making from Imbalanced Observational Data. CoRR (2019)
Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. IUI (2018)
Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier Gonzalez, Michael Riis Andersen
Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. MLSP (2018)
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 (2018)
Juho Piironen, Aki Vehtari
Iterative Supervised Principal Components. AISTATS (2018)
Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
Yes, but Did It Work?: Evaluating Variational Inference. ICML (2018)
Juho Piironen, Markus Paasiniemi, Aki Vehtari
Projective Inference in High-dimensional Problems: Prediction and Feature Selection. CoRR (2018)
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 (2017)
Aki Vehtari, Andrew Gelman, Jonah Gabry
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27 (5) (2017)
Juho Piironen, Aki Vehtari
Comparison of Bayesian predictive methods for model selection. Stat. Comput. 27 (3) (2017)
Aki Vehtari, Andrew Gelman, Jonah Gabry
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27 (5) (2017)
Juho Piironen, Aki Vehtari
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. AISTATS (2017)
Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. 18 (2017)
Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. CoRR (2017)
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 (2017)
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)
Juho Piironen, Aki Vehtari
Projection predictive model selection for Gaussian processes. MLSP (2016)
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 (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. AISTATS (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. CoRR (2016)
Andrew Gelman, Jessica Hwang, Aki Vehtari
Understanding predictive information criteria for Bayesian models. Stat. Comput. 24 (6) (2014)
Ville Tolvanen, Pasi Jylänki, Aki Vehtari
Expectation propagation for nonstationary heteroscedastic Gaussian process regression. MLSP (2014)
Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari
Expectation propagation for neural networks with sparsity-promoting priors. J. Mach. Learn. Res. 15 (1) (2014)
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)
Tomi Peltola, Pasi Jylänki, Aki Vehtari
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. AISTATS (2014)
Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari
Nested expectation propagation for Gaussian process classification. J. Mach. Learn. Res. 14 (1) (2013)
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari
GPstuff: Bayesian modeling with Gaussian processes. J. Mach. Learn. Res. 14 (1) (2013)
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 (2012)
Jarno Vanhatalo, Aki Vehtari
Speeding up the binary Gaussian process classification CoRR (2012)
Jarno Vanhatalo, Aki Vehtari
Modelling local and global phenomena with sparse Gaussian processes CoRR (2012)
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) (2012)
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
Likelihood. J. Mach. Learn. Res. 12 (2011)
Elina Parviainen, Aki Vehtari
Explaining Classification by Finding Response-Related Subgroups in Data. SNPD (2010)
Jaakko Riihimäki, Aki Vehtari
Gaussian processes with monotonicity information. AISTATS (2010)
Jarno Vanhatalo, Aki Vehtari
Speeding up the binary Gaussian process classification. UAI (2010)
Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari
Gaussian process regression with Student-t likelihood. NIPS (2009)
Elina Parviainen, Aki Vehtari
Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. ICANN (2) (2009)
Jarno Vanhatalo, Aki Vehtari
Modelling local and global phenomena with sparse Gaussian processes. UAI (2008)
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) (2007)
Jarno Vanhatalo, Aki Vehtari
Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology. Gaussian Processes in Practice (2007)
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski, Mika Ala-Korpela
H NMR metabonomic data. BMC Bioinform. 8 (S-2) (2007)
Simo Särkkä, Aki Vehtari, Jouko Lampinen
Rao-Blackwellized particle filter for multiple target tracking. Inf. Fusion 8 (1) (2007)
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)
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) (2007)
Simo Särkkä, Aki Vehtari, Jouko Lampinen
CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing 70 (13-15) (2007)
Ilkka Kalliomäki, Aki Vehtari, Jouko Lampinen
Shape analysis of concrete aggregates for statistical quality modeling. Mach. Vis. Appl. 16 (3) (2005)
Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, Mikko Sams
-norm priors. NeuroImage 26 (3) (2005)
Aki Vehtari, Jouko Lampinen
Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities. Neural Comput. 14 (10) (2002)
Jouko Lampinen, Aki Vehtari
Bayesian approach for neural networks--review and case studies. Neural Networks 14 (3) (2001)
Aki Vehtari, Simo Särkkä, Jouko Lampinen
On MCMC Sampling in Bayesian MLP Neural Networks. IJCNN (1) (2000)
Aki Vehtari, Jouko Lampinen
Bayesian MLP neural networks for image analysis. Pattern Recognit. Lett. 21 (13-14) (2000)
Jouko Lampinen, Aki Vehtari
Bayesian techniques for neural networks - Review and case studies. EUSIPCO (2000)
Aki Vehtari, Jouko Lampinen
Bayesian neural networks with correlating residuals. IJCNN (1999)
Jouko Lampinen, Aki Vehtari, Kimmo Leinonen
Application of Bayesian neural network in electrical impedance tomography. IJCNN (1999)