Hierarchically-partitioned Gaussian process approximation

Byung Jun Lee, Jongmin Lee, Kee Eung Kim

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

The Gaussian process (GP) is a simple yet powerful probabilistic framework for various machine learning tasks. However, exact algorithms for learning and prediction are prohibitive to be applied to large datasets due to inherent computational complexity. To overcome this main limitation, various techniques have been proposed, and in particular, local GP algorithms that scales”truly linearly” with respect to the dataset size. In this paper, we introduce a hierarchical model based on local GP for large-scale datasets, which stacks inducing points over inducing points in layers. By using different kernels in each layer, the overall model becomes multi-scale and is able to capture both long- and short-range dependencies. We demonstrate the effectiveness of our model by speed-accuracy performance on challenging real-world datasets.

Original languageEnglish
Publication statusPublished - 2017
Externally publishedYes
Event20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States
Duration: 2017 Apr 202017 Apr 22

Conference

Conference20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Country/TerritoryUnited States
CityFort Lauderdale
Period17/4/2017/4/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

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