Name
Abstract | ||
|---|---|---|
We study prediction problems in which the conditional distribution of the output given the input varies as a function of task variables which, in our applications, represent space and time. In varying-coefficient models, the coefficients of this conditional are allowed to change smoothly in space and time; the strength of the correlations between neighboring points is determined by the data. This is achieved by placing a Gaussian process (GP) prior on the coefficients. Bayesian inference in varying-coefficient models is generally intractable. We show that with an isotropic GP prior, inference in varying-coefficient models resolves to standard inference for a GP that can be solved efficiently. MAP inference in this model resolves to multitask learning using task and instance kernels. We clarify the relationship between varying-coefficient models and the hierarchical Bayesian multitask model and show that inference for hierarchical Bayesian multitask models can be carried out efficiently using graph-Laplacian kernels. We explore the model empirically for the problems of predicting rent and real-estate prices, and predicting the ground motion during seismic events. We find that varying-coefficient models with GP priors excel at predicting rents and real-estate prices. The ground-motion model predicts seismic hazards in the State of California more accurately than the previous state of the art. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10994-017-5639-3 | Machine Learning |
Keywords | Field | DocType |
Transfer learning,Varying-coefficient models,Housing-price prediction,Seismic-hazard models | Conditional probability distribution,Bayesian inference,Multi-task learning,Inference,Transfer of learning,Gaussian process,Artificial intelligence,Prior probability,Machine learning,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
106 | 9-10 | 0885-6125 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Bussas | 1 | 0 | 0.34 |
| Christoph Sawade | 2 | 55 | 6.21 |
| Nicolas Kühn | 3 | 0 | 0.34 |
| Tobias Scheffer | 4 | 1862 | 139.64 |
| Niels Landwehr | 5 | 506 | 31.54 |