TY  - JOUR
A1  - Bussas, Matthias
A1  - Sawade, Christoph
A1  - Kuhn, Nicolas
A1  - Scheffer, Tobias
A1  - Landwehr, Niels
T1  - Varying-coefficient models for geospatial transfer learning
JF  - Machine learning
N2  - 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.
KW  - Transfer learning
KW  - Varying-coefficient models
KW  - Housing-price prediction
KW  - Seismic-hazard models
Y1  - 2017
U6  - https://doi.org/10.1007/s10994-017-5639-3
SN  - 0885-6125
SN  - 1573-0565
VL  - 106
SP  - 1419
EP  - 1440
PB  - Springer
CY  - Dordrecht
ER  -