Learning Gradients with Gaussian Processes

Xinwei Jiang, Junbin Gao, Tianjiang Wang, Paul W. Kwan

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)


The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.
Original languageEnglish
Title of host publicationPAKDD 2010
EditorsVikram Pudi Vikram Pudi
Place of PublicationGermany
Number of pages12
Publication statusPublished - 2010
EventPacific-Asia Conference on Knowledge Discovery and Data Mining - Hyderabad, India, India
Duration: 21 Jun 201024 Jun 2010


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining

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    Jiang, X., Gao, J., Wang, T., & Kwan, P. W. (2010). Learning Gradients with Gaussian Processes. In V. P. V. Pudi (Ed.), PAKDD 2010 (Vol. 6119, pp. 113-124). Springer. https://doi.org/10.1007/978-3-642-13672-6_12