# Efficient Optimization for Sparse Gaussian Process Regression

arXiv: https://arxiv.org/abs/1310.6007 · HTML: https://mlsanity.com/paper/1310.6007/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2013.

## Built on by (most cited first, 8 of 8)

- [Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs](https://mlsanity.com/paper/2312.10975/) (2023)
- [Guaranteed Coverage Prediction Intervals with Gaussian Process Regression](https://mlsanity.com/paper/2310.15641/) (2023)
- [Semi-Parametric Inducing Point Networks and Neural Processes](https://mlsanity.com/paper/2205.11718/) (2022)
- [Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy](https://mlsanity.com/paper/2307.13945/) (2023)
- [Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning](https://mlsanity.com/paper/2402.14081/) (2024)
- [New Bounds for Sparse Variational Gaussian Processes](https://mlsanity.com/paper/2502.08730/) (2025)
- [Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data](https://mlsanity.com/paper/2111.02019/) (2021)
- [GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds](https://mlsanity.com/paper/2303.15225/) (2023)

## Abstract (arXiv)

We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates an inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy. The space and time complexity are linear in training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains. Empirical evaluation shows state-of-art performance in discrete cases and competitive results in the continuous case.