mlsanityData to 7 Oct 2026

Hierarchical Non-Stationary Temporal Gaussian Processes With L1-Regularization

arXiv 2105.09695 ↗First posted 20 May 2021stat.ME stat.CO stat.MLcited by 1

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Abstract (arXiv)

This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e.g., length-scale) are modeled as GPs. In particular, we consider two commonly used NSGP constructions which are based on explicitly constructed non-stationary covariance functions and stochastic differential equations, respectively. We extend these NSGPs by including L1-regularization on the processes in order to induce sparseness. To solve the resulting regularized NSGP (R-NSGP) regression problem we develop a method based on the alternating direction method of multipliers (ADMM) and we also analyze its convergence properties theoretically. We also evaluate the performance of the proposed methods in simulated and real-world datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 59 references that are in the map. First posted on arXiv 20 May 2021.

Builds on

17 of 59 references.

References · 17

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5 more
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Citing papers per year: 2021: 1.

1 paper.

  1. 2021State-space deep Gaussian processes with applicationscited by 2