Vehtari, Aki

39 publications

TMLR 2026 Amortized Bayesian Workflow Chengkun Li, Aki Vehtari, Paul-Christian Bürkner, Stefan T. Radev, Luigi Acerbi, Marvin Schmitt

AISTATS 2025 Posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner, Lu Zhang, Bob Carpenter, Aki Vehtari

JMLR 2024 A Framework for Improving the Reliability of Black-Box Variational Inference Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

NeurIPSW 2024 Amortized Bayesian Workflow (Extended Abstract) Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev

JMLR 2024 Pareto Smoothed Importance Sampling Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

AISTATS 2022 Feature Collapsing for Gaussian Process Variable Ranking Isaac Sebenius, Topi Paananen, Aki Vehtari

AISTATS 2022 Projection Predictive Inference for Generalized Linear and Additive Multilevel Models Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari

JMLR 2022 Pathfinder: Parallel Quasi-Newton Variational Inference Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari

JMLR 2022 Stacking for Non-Mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao, Aki Vehtari, Andrew Gelman

NeurIPS 2021 Challenges and Opportunities in High Dimensional Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R Andersen, Jonathan Huggins, Aki Vehtari

ICMLW 2021 Challenges for BBVI with Normalizing Flows Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari

UAI 2021 Uncertainty-Aware Sensitivity Analysis Using Rényi Divergences Topi Paananen, Michael Riis Andersen, Aki Vehtari

MLJ 2020 A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski

UAI 2020 Batch Simulations and Uncertainty Quantification in Gaussian Process Surrogate Approximate Bayesian Computation Marko Jarvenpaa, Aki Vehtari, Pekka Marttinen

JMLR 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

NeurIPS 2020 Hamiltonian Monte Carlo Using an Adjoint-Differentiated Laplace Approximation: Bayesian Inference for Latent Gaussian Models and Beyond Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

AISTATS 2020 Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Andersen

NeurIPS 2020 Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Michael R Andersen, Måns Magnusson, Jonathan Huggins, Aki Vehtari

ICML 2019 Active Learning for Decision-Making from Imbalanced Observational Data Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski

ICML 2019 Bayesian Leave-One-Out Cross-Validation for Large Data Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari

AISTATS 2019 Variable Selection for Gaussian Processes via Sensitivity Analysis of the Posterior Predictive Distribution Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari

MLOSS 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

AISTATS 2018 Iterative Supervised Principal Components Juho Piironen, Aki Vehtari

ICML 2018 Yes, but Did It Work?: Evaluating Variational Inference Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

JMLR 2017 Bayesian Inference for Spatio-Temporal Spike-and-Slab Priors Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen

AISTATS 2017 On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior Juho Piironen, Aki Vehtari

JMLR 2016 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther

AISTATS 2016 Chained Gaussian Processes Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

AISTATS 2014 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables Tomi Peltola, Pasi Jylänki, Aki Vehtari

JMLR 2014 Expectation Propagation for Neural Networks with Sparsity-Promoting Priors Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari

UAI 2014 Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction Tomi Peltola, Aki S. Havulinna, Veikko Salomaa, Aki Vehtari

MLOSS 2013 GPstuff: Bayesian Modeling with Gaussian Processes Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari

JMLR 2013 Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari

JMLR 2011 Robust Gaussian Process Regression with a Student-T Likelihood Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari

AISTATS 2010 Gaussian Processes with Monotonicity Information Jaakko Riihimäki, Aki Vehtari

UAI 2010 Speeding up the Binary Gaussian Process Classification Jarno Vanhatalo, Aki Vehtari

NeurIPS 2009 Gaussian Process Regression with Student-T Likelihood Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari

UAI 2008 Modelling Local and Global Phenomena with Sparse Gaussian Processes Jarno Vanhatalo, Aki Vehtari

NeCo 2002 Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities Aki Vehtari, Jouko Lampinen