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- Title
Heterogeneous multi-task Gaussian Cox processes.
- Authors
Zhou, Feng; Kong, Quyu; Deng, Zhijie; He, Fengxiang; Cui, Peng; Zhu, Jun
- Abstract
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.
- Subjects
VANCOUVER (B.C.); GAUSSIAN processes; POINT processes; NONPARAMETRIC estimation; DATA augmentation; PARAMETER estimation; BAYESIAN field theory; INFORMATION sharing
- Publication
Machine Learning, 2023, Vol 112, Issue 12, p5105
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-023-06382-1