Heterogeneous Multi-Task Gaussian Cox Processes
This work addresses the challenge of integrating diverse data types in multi-task learning for applications such as urban analysis, though it is incremental as it extends existing Gaussian Cox process methods.
The paper tackled the problem of jointly modeling multiple heterogeneous correlated tasks like classification and regression using multi-task Gaussian Cox processes, achieving improved performance through information sharing and nonparametric estimation, with demonstrations on synthetic and urban data.
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via multi-output Gaussian processes (MOGP). A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point process tasks can facilitate sharing of information between heterogeneous tasks, while allowing for nonparametric parameter estimation. To circumvent the non-conjugate Bayesian inference in the MOGP modulated heterogeneous multi-task framework, we employ the data augmentation technique and derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters. We demonstrate the performance and inference on both 1D synthetic data as well as 2D urban data of Vancouver.