Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
This addresses computational efficiency for researchers and practitioners using Gaussian processes in temporal modeling, though it is incremental as it builds on existing sparse precision and state-space formulations.
The authors tackled the computational bottleneck in Gaussian process temporal models by developing SpInGP, a parallelizable sparse inverse formulation that achieves linear time complexity and sublinear parallel performance, demonstrating effectiveness on simulated and real data.
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.