We investigate nonlinear regression and introduce a novel approach based on the joint optimization of linear models. In order to capture both characteristics in sequential data, we model underlying data as combination models, where optimize models jointly minimize final error. As model, employ differentiable version boosted decision trees. use well-known SARIMAX model. Our is generic so that an...

Abstract We investigate jointly modelling age–year-specific rates of various causes death in a multinational setting. apply multi-output Gaussian processes (MOGPs), spatial machine learning method, to smooth and extrapolate multiple cause-of-death mortality across several countries both genders. To maintain flexibility scalability, we MOGPs with Kronecker-structured kernels latent factors. In p...

In Quaternary deposits, the characterization of subsurface heterogeneity and its associated uncertainty is critical when dealing with groundwater resource management. The combination different data types through joint inversion has proven to be an effective way reduce final model uncertainty. Moreover, it allows in agreement a wider spectrum available on site. However, integrating them stochast...