Name
Abstract | ||
|---|---|---|
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables. In varying-coefficient models with Gaussian process priors, a Gaussian process generates the functional relationship between the task variables and the parameters of this conditional. Varying-coefficient models subsume hierarchical Bayesian multitask models, but also generalizations in which the conditional varies continuously, for instance, in time or space. However, Bayesian inference in varying-coefficient models is generally intractable. We show that inference for varying-coefficient models with isotropic Gaussian process priors resolves to standard inference for a Gaussian process that can be solved efficiently. MAP inference in this model resolves to multitask learning using task and instance kernels, and inference for hierarchical Bayesian multitask models can be carried out efficiently using graph-Laplacian kernels. We report on experiments for geospatial prediction. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Frequentist inference,Conditional probability distribution,Multi-task learning,Bayesian inference,Pattern recognition,Inference,Gaussian process,Artificial intelligence,Prior probability,Mathematics,Machine learning,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1508.07192 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| matthias bussas | 1 | 0 | 0.34 |
| Christoph Sawade | 2 | 55 | 6.21 |
| Tobias Scheffer | 3 | 1862 | 139.64 |
| Niels Landwehr | 4 | 506 | 31.54 |