Aki Vehtari
31 papers · 2009–2025 · 5 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (14) 🌍 Conference Polyglot (5)
🐣
Hot Topic Early Bird
🌉
Interdisciplinary Bridge
🧭
Keyword Pioneer
🔬
Deep Specialist
(20)
🏆
Keyword Champion
(4)
🗃️
Keyword Collector
(107)
⚡
Prolific Year
(5)
🚀
Conference Pioneer
📈
Trend Setter
💎
Century Club
(31)
🔥
Unstoppable
(7)
❓
The Questioner
Conferences
JMLR (12)
AISTATS (10)
NIPS (4)
ICML (3)
UAI (2)
Top co-authors
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 Rényi 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