Open AccessProceedings Article
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning
Miguel Lázaro-Gredilla,Michalis K. Titsias +1 more
- Vol. 24, pp 2339-2347
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TLDR
A variational Bayesian inference algorithm which can be widely applied to sparse linear models and is based on the spike and slab prior, which is the golden standard for sparse inference is introduced.Abstract:
We introduce a variational Bayesian inference algorithm which can be widely applied to sparse linear models. The algorithm is based on the spike and slab prior which, from a Bayesian perspective, is the golden standard for sparse inference. We apply the method to a general multi-task and multiple kernel learning model in which a common set of Gaussian process functions is linearly combined with task-specific sparse weights, thus inducing relation between tasks. This model unifies several sparse linear models, such as generalized linear models, sparse factor analysis and matrix factorization with missing values, so that the variational algorithm can be applied to all these cases. We demonstrate our approach in multi-output Gaussian process regression, multi-class classification, image processing applications and collaborative filtering.read more
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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
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TL;DR: This work presents Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data that reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints.
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