| 2025 | AISTATS | posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. | Mns Magnusson, Jakob Torgander, Paul-Christian Brkner, Lu Zhang, Bob Carpenter, Aki Vehtari |
| 2022 | AISTATS | Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. | Alejandro Catalina, Paul-Christian Brkner, Aki Vehtari |
| 2022 | AISTATS | Feature Collapsing for Gaussian Process Variable Ranking. | Isaac Sebenius, Topi Paananen, Aki Vehtari |
| 2021 | UAI | Uncertainty-aware sensitivity analysis using Rnyi divergences. | Topi Paananen, Michael Riis Andersen, Aki Vehtari |
| 2020 | AISTATS | Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. | Mns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen |
| 2020 | UAI | Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. | Marko Jrvenp, Aki Vehtari, Pekka Marttinen |
| 2019 | AISTATS | Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. | Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari |
| 2019 | ICML | Bayesian leave-one-out cross-validation for large data. | Mns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari |
| 2019 | ICML | Active Learning for Decision-Making from Imbalanced Observational Data. | Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski |
| 2018 | AISTATS | Iterative Supervised Principal Components. | Juho Piironen, Aki Vehtari |
| 2018 | ICML | Yes, but Did It Work?: Evaluating Variational Inference. | Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman |
| 2018 | IUI | User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. | Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski |
| 2017 | AISTATS | On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. | Juho Piironen, Aki Vehtari |
| 2016 | AISTATS | Chained Gaussian Processes. | Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence |
| 2014 | AISTATS | Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. | Tomi Peltola, Pasi Jylnki, Aki Vehtari |
| 2014 | UAI | Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction. | Tomi Peltola, Aki S. Havulinna, Veikko Salomaa, Aki Vehtari |
| 2010 | UAI | Speeding up the binary Gaussian process classification. | Jarno Vanhatalo, Aki Vehtari |
| 2010 | SNPD | Explaining Classification by Finding Response-Related Subgroups in Data. | Elina Parviainen, Aki Vehtari |
| 2009 | ICANN | Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. | Elina Parviainen, Aki Vehtari |
| 2008 | UAI | Modelling local and global phenomena with sparse Gaussian processes. | Jarno Vanhatalo, Aki Vehtari |
| 2007 | ISMB | 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-Jrvinen, Matej Oresic |
| 2004 | IJCNN | Time series prediction by Kalman smoother with cross-validated noise density. | Simo Srkk, Aki Vehtari, Jouko Lampinen |
| 2004 | IJCNN | Time series prediction by Kalman smoother with cross-validated noise density. | Simo Srkk, Aki Vehtari, Jouko Lampinen |
| 2000 | IJCNN | On MCMC Sampling in Bayesian MLP Neural Networks. | Aki Vehtari, Simo Srkk, Jouko Lampinen |
| 1999 | IJCNN | Application of Bayesian neural network in electrical impedance tomography. | Jouko Lampinen, Aki Vehtari, Kimmo Leinonen |
| 1999 | IJCNN | Bayesian neural networks with correlating residuals. | Aki Vehtari, Jouko Lampinen |