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Aki Vehtari

31 papers · 2009–2025 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) πŸƒ Academic Marathon (16) πŸ—ΊοΈ Taxonomy Completionist (14) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (4) πŸ—ƒοΈ Keyword Collector (107) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (7) ❓ The Questioner

Conferences

JMLR (12) AISTATS (10) NIPS (4) ICML (3) UAI (2)

Top co-authors

Pasi JylΓ€nki (7) Andrew Gelman (5) Michael Riis Andersen (4) MΓ₯ns Magnusson (4) Jaakko RiihimΓ€ki (3) Topi Paananen (3) Yuling Yao (3) Jarno Vanhatalo (3) Daniel Simpson (3) Juho Piironen (3)

Keywords

bayesian inference (12) gaussian process (10) variational inference (9) expectation propagation (8) approximate inference (5) markov chain monte carlo (4) posterior approximation (4) sensitivity analysis (3) latent variable model (3) importance sampling (3) laplace approximation (3) predictive performance (3) leave-one-out cross-validation (3) hyperparameter optimization (2) robust regression (2) student-t likelihood (2) bayesian model comparison (2) gaussian process regression (2) stochastic optimization (2) variable selection (2)

Papers

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms AISTATS 2025
Pareto Smoothed Importance Sampling JMLR 2024
A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors JMLR 2022
Pathfinder: Parallel quasi-Newton variational inference JMLR 2022
Feature Collapsing for Gaussian Process Variable Ranking AISTATS 2022
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models AISTATS 2022
Challenges and Opportunities in High Dimensional Variational Inference NIPS 2021
Uncertainty-aware sensitivity analysis using RΓ©nyi divergences UAI 2021
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NIPS 2020
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data AISTATS 2020
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation UAI 2020
Robust, Accurate Stochastic Optimization for Variational Inference NIPS 2020
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
Active Learning for Decision-Making from Imbalanced Observational Data ICML 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
Chained Gaussian Processes AISTATS 2016
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models JMLR 2016
Expectation Propagation for Neural Networks with Sparsity-Promoting Priors JMLR 2014
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 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 NIPS 2009