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• W3166364057 abstract "Currently, multi-output Gaussian process regression models either do notmodel nonstationarity or are associated with severe computational burdens andstorage demands. Nonstationary multi-variate Gaussian process models (NMGP) usea nonstationary covariance function with an input-dependent linear model ofcoregionalisation to jointly model input-dependent correlation, scale, andsmoothness of outputs. Variational sparse approximation relies on inducingpoints to enable scalable computations. Here, we take the best of both worlds:considering an inducing variable framework on the underlying latent functionsin NMGP, we propose a novel model called the collaborative nonstationaryGaussian process model(CNMGP). For CNMGP, we derive computationally tractablevariational bounds amenable to doubly stochastic variational inference.Together, this allows us to model data in which outputs do not share a commoninput set, with a computational complexity that is independent of the size ofthe inputs and outputs. We illustrate the performance of our method onsynthetic data and three real datasets and show that our model generallypro-vides better predictive performance than the state-of-the-art, and alsoprovides estimates of time-varying correlations that differ across outputs." @default.
• W3166364057 created "2021-06-22" @default.
• W3166364057 creator A5040319820 @default.
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• W3166364057 date "2021-06-01" @default.
• W3166364057 modified "2023-09-27" @default.
• W3166364057 title "Collaborative Nonstationary Multivariate Gaussian Process Model." @default.
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