1 code implementation • 23 Apr 2020 • Khaoula El Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski
We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD).
no code implementations • 6 Apr 2020 • Tom Vander Aa, Xiangju Qin, Paul Blomstedt, Roel Wuyts, Wilfried Verachtert, Samuel Kaski
We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.
no code implementations • 31 Jul 2019 • 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 sparsely observed.
no code implementations • 11 Mar 2019 • 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.
no code implementations • 2 Mar 2017 • Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.
no code implementations • 30 Mar 2016 • Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.
1 code implementation • 29 Sep 2015 • Paul Blomstedt, Jarno Vanhatalo, Mats Ulmestrand, Anna Gårdmark, Samu Mäntyniemi
We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis).
Applications
no code implementations • 19 May 2015 • Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma, Samuel Kaski
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
2 code implementations • 16 Dec 2014 • Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert
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 obtain a global posterior approximation.