64 citations · 99 across the 10 of their papers we have counts for
10 papers
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
Tom Vander Aa, Xiangju Qin, Paul Blomstedt +3
Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability…
Federated Stochastic Gradient Langevin Dynamics
Khaoula El Mekkaoui, Diego Mesquita, Paul Blomstedt +1
Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although…
Scalable Bayesian Non-linear Matrix Completion
Xiangju Qin, Paul Blomstedt, Samuel Kaski
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparse…
Meta-analysis of Bayesian analyses
Paul Blomstedt, Diego Mesquita, Omar Rivasplata +4
Meta-analysis aims to generalize results from multiple related statistical analyses through a combined analysis. While the natural outcome of a Bayesian study is a posterior distri…
Embarrassingly parallel MCMC using deep invertible transformations
Diego Mesquita, Paul Blomstedt, Samuel Kaski
While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge. Embarrassingly parallel MCMC strategies ta…
Distributed Bayesian Matrix Factorization with Limited Communication
Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho +2
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scal…
Bayesian inference in hierarchical models by combining independent posteriors
Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally…
A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis)
Paul Blomstedt, Jarno Vanhatalo, Mats Ulmestrand +2
We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis). This has the advantage of structuring the population in terms o…
Modelling-based experiment retrieval: A case study with gene expression clustering
Paul Blomstedt, Ritabrata Dutta, Sohan Seth +2
Motivation: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art o…
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula +7
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to…