Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains

2022-02-25 · Haitao Liu, Kai Wu, Yew-Soon Ong, Chao Bian, Xiaomo Jiang, Xiaofang Wang

Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs are usually limited to the multi-task scenario defined in the same input domain, leaving no space for tackling the heterogeneous case, i.e., the features of input domains vary over tasks. To this end, this paper presents a novel heterogeneous stochastic variational linear model of coregionalization (HSVLMC) model for simultaneously learning the tasks with varied input domains. Particularly, we develop the stochastic variational framework with Bayesian calibration that (i) takes into account the effect of dimensionality reduction raised by domain mappings in order to achieve effective input alignment; and (ii) employs a residual modeling strategy to leverage the inductive bias brought by prior domain mappings for better model inference. Finally, the superiority of the proposed model against existing LMC models has been extensively verified on diverse heterogeneous multi-task cases and a practical multi-fidelity steam turbine exhaust problem.

📄 PDF Abstract BibTeX arXiv:2202.12636

Code (0)

등록된 구현이 없습니다.

Tasks

Dimensionality ReductionInductive Bias

Methods 이 논문이 사용한 방법론

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

Heterogeneous Multi-Task Gaussian Cox Processes

2023-08-29 · Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He 외

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 …

Bayesian Inference Data Augmentation Gaussian Processes parameter estimation+1

Information fusion in multi-task Gaussian processes

2012-10-06 · Shrihari Vasudevan, Arman Melkumyan, Steven Scheding

This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across hete…

Gaussian Processes

Heterogeneous Multi-output Gaussian Process Prediction

2018-05-19 · NeurIPS 2018 12 · Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez

We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly mod…

Gaussian Processes Prediction Variational Inference

Similarity Gaussian Process Latent Variable Model for Multi-Modal Data Analysis

2015-12-01 · ICCV 2015 12 · Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian

Data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects. However, relations among heterogeneous modalities are simply t…

Retrieval

R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning

2025-12-11 · Duo Wang, Xinming Wang, Chao Wang, Xiaowei Yue 외 arxiv

Multi-output Gaussian process (MGP) models have attracted significant attention for their flexibility and uncertainty-quantification capabilities, and have been widely adopted in multi-source transfer learning scenarios …

Transfer Learning