# Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

arXiv: https://arxiv.org/abs/1610.08035 · HTML: https://mlsanity.com/paper/1610.08035/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

## Built on by (most cited first, 10 of 10)

- [Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes](https://mlsanity.com/paper/2106.10210/) (2021)
- [Correlated Product of Experts for Sparse Gaussian Process Regression](https://mlsanity.com/paper/2112.09519/) (2021)
- [Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data](https://mlsanity.com/paper/2109.14171/) (2021)
- [State-space deep Gaussian processes with applications](https://mlsanity.com/paper/2111.12604/) (2021)
- [Temporal Gaussian Process Regression in Logarithmic Time](https://mlsanity.com/paper/2102.09964/) (2021)
- [On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach](https://mlsanity.com/paper/2103.03321/) (2021)
- [Privacy-aware Gaussian Process Regression](https://mlsanity.com/paper/2305.16541/) (2023)
- [Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner](https://mlsanity.com/paper/2408.00206/) (2024)
- [Aggregation Models with Optimal Weights for Distributed Gaussian Processes](https://mlsanity.com/paper/2408.00955/) (2024)
- [On the Sequentially Semiseparable Structure of SDE-Induced Kernels and Efficient Algorithms for Kernel-Based Estimation Problems](https://mlsanity.com/paper/2610.05856/) (2026)

## Abstract (arXiv)

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and because all the computations are parallelizable, the parallel form of the algorithm is sublinear in the number of data points. We provide example algorithms to implement the sparse matrix routines and experimentally test the method using both simulated and real data.