Aki Vehtari - RankMe

108 papers A* 6A 15B 4C 1Journal 69Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Amortized Bayesian Workflow. Trans. Mach. Learn. Res. Chengkun Li, Aki Vehtari, Paul-Christian Bürkner, Stefan T. Radev, Luigi Acerbi, Marvin Schmitt
2026	J	`jnl`	ArviZ: a modular and flexible library for exploratory analysis of Bayesian models. J. Open Source Softw. Osvaldo A. Martin, Oriol Abril Pla, Jordan Deklerk, Seth D. Axen, Colin Carroll, Ari Hartikainen, Aki Vehtari
2026	J	`jnl`	Posterior SBC: simulation-based calibration checking conditional on data. Stat. Comput. Teemu Säilynoja, Marvin Schmitt, Paul-Christian Bürkner, Aki Vehtari
2025	J	`jnl`	Projection predictive variable selection for discrete response families with finite support. Comput. Stat. Frank Weber, Änne Glass, Aki Vehtari
2025	A	`conf`	posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. AISTATS Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner, Lu Zhang, Bob Carpenter, Aki Vehtari
2024	J	`jnl`	A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2024	J	`jnl`	Active Learning of Molecular Data for Task-Specific Objectives. CoRR Kunal Ghosh, Milica Todorovic, Aki Vehtari, Patrick Rinke
2024	J	`jnl`	Amortized Bayesian Workflow (Extended Abstract). CoRR Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev
2024	J	`jnl`	Bayesian cross-validation by parallel Markov chain Monte Carlo. Stat. Comput. Alex Cooper, Aki Vehtari, Catherine Forbes, Daniel Simpson, Lauren Kennedy
2024	J	`jnl`	Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Stat. Comput. Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari
2024	J	`jnl`	Efficient estimation and correction of selection-induced bias with order statistics. Stat. Comput. Yann McLatchie, Aki Vehtari
2024	J	`jnl`	Pareto Smoothed Importance Sampling. J. Mach. Learn. Res. Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry
2024	J	`jnl`	Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. Ecol. Informatics Ryoko Noda, Michael Mechenich, Juha Saarinen, Aki Vehtari, Indre Zliobaite
2023	J	`jnl`	Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Stat. Comput. Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael Riis Andersen, Arno Solin, Aki Vehtari
2023	J	`jnl`	Using reference models in variable selection. Comput. Stat. Federico Pavone, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari
2022	J	`jnl`	A fast regression via SVD and marginalization. Comput. Stat. Philip Greengard, Andrew Gelman, Aki Vehtari
2022	J	`jnl`	Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort. NeuroImage Tuulia Malén, Tomi Karjalainen, Janne Isojärvi, Aki Vehtari, Paul-Christian Bürkner, Vesa Putkinen, Valtteri Kaasinen, Jarmo Hietala, Pirjo Nuutila, Juha O. Rinne, Lauri Nummenmaa
2022	A	`conf`	Feature Collapsing for Gaussian Process Variable Ranking. AISTATS Isaac Sebenius, Topi Paananen, Aki Vehtari
2022	J	`jnl`	Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. Stat. Comput. Teemu Säilynoja, Paul-Christian Bürkner, Aki Vehtari
2022	J	`jnl`	Pathfinder: Parallel quasi-Newton variational inference. J. Mach. Learn. Res. Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
2022	A	`conf`	Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. AISTATS Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari
2022	J	`jnl`	Robust, Automated, and Accurate Black-box Variational Inference. CoRR Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2022	J	`jnl`	Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. J. Mach. Learn. Res. Yuling Yao, Aki Vehtari, Andrew Gelman
2021	J	`jnl`	Bayesian hierarchical stacking. CoRR Yuling Yao, Gregor Pirs, Aki Vehtari, Andrew Gelman
2021	A*	`conf`	Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2021	J	`jnl`	Challenges and Opportunities in High-dimensional Variational Inference. CoRR Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2021	J	`jnl`	Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models. Comput. Stat. Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari
2021	J	`jnl`	Implicitly adaptive importance sampling. Stat. Comput. Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari
2021	J	`jnl`	Pathfinder: Parallel quasi-Newton variational inference. CoRR Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
2021	—	`conf`	Preferential Batch Bayesian Optimization. MLSP Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
2021	A	`conf`	Uncertainty-aware sensitivity analysis using Rényi divergences. UAI Topi Paananen, Michael Riis Andersen, Aki Vehtari
2021	J	`jnl`	lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinform. Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
2020	J	`jnl`	A Fast Linear Regression via SVD and Marginalization. CoRR Philip Greengard, Andrew Gelman, Aki Vehtari
2020	J	`jnl`	A decision-theoretic approach for model interpretability in Bayesian framework. Mach. Learn. Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski
2020	A	`conf`	Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. UAI Marko Järvenpää, Aki Vehtari, Pekka Marttinen
2020	J	`jnl`	Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
2020	J	`jnl`	Good practices for Bayesian Optimization of high dimensional structured spaces. CoRR Eero Siivola, Javier González, Andrei Paleyes, Aki Vehtari
2020	A*	`conf`	Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. NeurIPS Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal
2020	J	`jnl`	Interindividual variability and lateralization of μ-opioid receptors in the human brain. NeuroImage 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
2020	A	`conf`	Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. AISTATS Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen
2020	J	`jnl`	Preferential Batch Bayesian Optimization. CoRR Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
2020	A*	`conf`	Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020	J	`jnl`	Robust, Accurate Stochastic Optimization for Variational Inference. CoRR Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020	—	`conf`	Scalable Gaussian Process for Extreme Classification. MLSP Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari
2019	A*	`conf`	Active Learning for Decision-Making from Imbalanced Observational Data. ICML Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
2019	J	`jnl`	Active Learning for Decision-Making from Imbalanced Observational Data. CoRR Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
2019	J	`jnl`	An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. CoRR Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
2019	J	`jnl`	Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. CoRR Marko Järvenpää, Aki Vehtari, Pekka Marttinen
2019	A*	`conf`	Bayesian leave-one-out cross-validation for large data. ICML Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
2019	J	`jnl`	Bayesian leave-one-out cross-validation for large data. CoRR Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
2019	J	`jnl`	Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. CoRR Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski
2019	J	`jnl`	Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen
2019	A	`conf`	Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. AISTATS Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari
2018	—	`conf`	Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. MLSP Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen
2018	J	`jnl`	ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
2018	A	`conf`	Iterative Supervised Principal Components. AISTATS Juho Piironen, Aki Vehtari
2018	J	`jnl`	Projective Inference in High-dimensional Problems: Prediction and Feature Selection. CoRR Juho Piironen, Markus Paasiniemi, Aki Vehtari
2018	A	`conf`	User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. IUI Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
2018	A*	`conf`	Yes, but Did It Work?: Evaluating Variational Inference. ICML Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
2017	J	`jnl`	Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
2017	J	`jnl`	Comparison of Bayesian predictive methods for model selection. Stat. Comput. Juho Piironen, Aki Vehtari
2017	J	`jnl`	Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex. NeuroImage 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
2017	J	`jnl`	ELFI: Engine for Likelihood Free Inference. CoRR Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
2017	J	`jnl`	Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. Aki Vehtari, Andrew Gelman, Jonah Gabry
2017	A	`conf`	On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. AISTATS Juho Piironen, Aki Vehtari
2017	J	`jnl`	Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. Aki Vehtari, Andrew Gelman, Jonah Gabry
2017	J	`jnl`	User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. CoRR Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
2016	—	`conf`	Automatic detection of acute kidney injury episodes from primary care data. SSCI Santosh Tirunagari, Simon C. Bull, Aki Vehtari, Christopher Farmer, Simon de Lusignan, Norman Poh
2016	J	`jnl`	Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models. J. Mach. Learn. Res. Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther
2016	A	`conf`	Chained Gaussian Processes. AISTATS Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
2016	J	`jnl`	Chained Gaussian Processes. CoRR Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
2016	—	`conf`	Projection predictive model selection for Gaussian processes. MLSP Juho Piironen, Aki Vehtari
2014	A	`conf`	Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. AISTATS Tomi Peltola, Pasi Jylänki, Aki Vehtari
2014	J	`jnl`	Expectation propagation for neural networks with sparsity-promoting priors. J. Mach. Learn. Res. Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari
2014	—	`conf`	Expectation propagation for nonstationary heteroscedastic Gaussian process regression. MLSP Ville Tolvanen, Pasi Jylänki, Aki Vehtari
2014	—	`conf`	Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction. BMA@UAI Tomi Peltola, Aki S. Havulinna, Veikko Salomaa, Aki Vehtari
2014	J	`jnl`	Understanding predictive information criteria for Bayesian models. Stat. Comput. Andrew Gelman, Jessica Hwang, Aki Vehtari
2013	J	`jnl`	GPstuff: Bayesian modeling with Gaussian processes. J. Mach. Learn. Res. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari
2013	J	`jnl`	Nested expectation propagation for Gaussian process classification. J. Mach. Learn. Res. Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari
2012	J	`jnl`	Bayesian Modeling with Gaussian Processes using the MATLAB Toolbox GPstuff (v3.3) CoRR Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari
2012	J	`jnl`	Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, Fa-Hsuan Lin
2012	J	`jnl`	Modelling local and global phenomena with sparse Gaussian processes CoRR Jarno Vanhatalo, Aki Vehtari
2012	J	`jnl`	Speeding up the binary Gaussian process classification CoRR Jarno Vanhatalo, Aki Vehtari
2011	J	`jnl`	Robust Gaussian Process Regression with a Student- J. Mach. Learn. Res. Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
2010	C	`conf`	Explaining Classification by Finding Response-Related Subgroups in Data. SNPD Elina Parviainen, Aki Vehtari
2010	A	`conf`	Gaussian processes with monotonicity information. AISTATS Jaakko Riihimäki, Aki Vehtari
2010	A	`conf`	Speeding up the binary Gaussian process classification. UAI Jarno Vanhatalo, Aki Vehtari
2009	—	`conf`	Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. ICANN (2) Elina Parviainen, Aki Vehtari
2009	—	`conf`	Gaussian process regression with Student-t likelihood. NIPS Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari
2008	A	`conf`	Modelling local and global phenomena with sparse Gaussian processes. UAI Jarno Vanhatalo, Aki Vehtari
2007	J	`jnl`	A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in BMC Bioinform. Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski, Mika Ala-Korpela
2007	J	`jnl`	Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. NeuroImage Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Mikko Sams, Aki Vehtari, Jouko Lampinen
2007	J	`jnl`	CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing Simo Särkkä, Aki Vehtari, Jouko Lampinen
2007	—	`conf`	Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. ISMB/ECCB (Supplement of Bioinformatics) Marko Sysi-Aho, Aki Vehtari, Vidya R. Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen, Matej Oresic
2007	J	`jnl`	Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods. NeuroImage Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Mikko Sams, Aki Vehtari
2007	J	`jnl`	Rao-Blackwellized particle filter for multiple target tracking. Inf. Fusion Simo Särkkä, Aki Vehtari, Jouko Lampinen
2007	—	`conf`	Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology. Gaussian Processes in Practice Jarno Vanhatalo, Aki Vehtari
2005	J	`jnl`	Bayesian analysis of the neuromagnetic inverse problem with ℓ NeuroImage Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, Mikko Sams
2005	J	`jnl`	Shape analysis of concrete aggregates for statistical quality modeling. Mach. Vis. Appl. Ilkka Kalliomäki, Aki Vehtari, Jouko Lampinen
2004	B	`conf`	Time series prediction by Kalman smoother with cross-validated noise density. IJCNN Simo Särkkä, Aki Vehtari, Jouko Lampinen
2004	B	`conf`	Time series prediction by Kalman smoother with cross-validated noise density. IJCNN Simo Särkkä, Aki Vehtari, Jouko Lampinen
2002	J	`jnl`	Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities. Neural Comput. Aki Vehtari, Jouko Lampinen
2001	J	`jnl`	Bayesian approach for neural networks--review and case studies. Neural Networks Jouko Lampinen, Aki Vehtari
2000	J	`jnl`	Bayesian MLP neural networks for image analysis. Pattern Recognit. Lett. Aki Vehtari, Jouko Lampinen
2000	—	`conf`	Bayesian techniques for neural networks - Review and case studies. EUSIPCO Jouko Lampinen, Aki Vehtari
2000	—	`conf`	On MCMC Sampling in Bayesian MLP Neural Networks. IJCNN (1) Aki Vehtari, Simo Särkkä, Jouko Lampinen
1999	B	`conf`	Application of Bayesian neural network in electrical impedance tomography. IJCNN Jouko Lampinen, Aki Vehtari, Kimmo Leinonen
1999	B	`conf`	Bayesian neural networks with correlating residuals. IJCNN Aki Vehtari, Jouko Lampinen