Aki Vehtari

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms AISTATS 2025 A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024 Pareto Smoothed Importance Sampling JMLR 2024 Feature Collapsing for Gaussian Process Variable Ranking AISTATS 2022 Pathfinder: Parallel quasi-Newton variational inference JMLR 2022 Projection Predictive Inference for Generalized Linear and Additive Multilevel Models AISTATS 2022 Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors JMLR 2022 Challenges and Opportunities in High Dimensional Variational Inference NeurIPS 2021 Uncertainty-aware sensitivity analysis using Rényi divergences UAI 2021 Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation UAI 2020 Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020 Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NeurIPS 2020 Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data AISTATS 2020 Robust, Accurate Stochastic Optimization for Variational Inference NeurIPS 2020 Active Learning for Decision-Making from Imbalanced Observational Data ICML 2019 Bayesian leave-one-out cross-validation for large data ICML 2019 Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution AISTATS 2019 ELFI: Engine for Likelihood-Free Inference JMLR 2018 Iterative Supervised Principal Components AISTATS 2018 Yes, but Did It Work?: Evaluating Variational Inference ICML 2018 Bayesian Inference for Spatio-temporal Spike-and-Slab Priors JMLR 2017 On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior AISTATS 2017 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models JMLR 2016 Chained Gaussian Processes AISTATS 2016 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014 Expectation Propagation for Neural Networks with Sparsity-Promoting Priors JMLR 2014 GPstuff: Bayesian Modeling with Gaussian Processes JMLR 2013 Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood JMLR 2013 Robust Gaussian Process Regression with a Student- Likelihood JMLR 2011 Gaussian processes with monotonicity information AISTATS 2010 Gaussian process regression with Student-t likelihood NeurIPS 2009 Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology AISTATS 1997