# Blitzkriging: Kronecker-structured Stochastic Gaussian Processes

arXiv: https://arxiv.org/abs/1510.07965 · HTML: https://mlsanity.com/paper/1510.07965/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process](https://mlsanity.com/paper/2206.06357/) (2022)
- [Tensor network square root Kalman filter for online Gaussian process regression](https://mlsanity.com/paper/2409.03276/) (2024)
- [Low-rank computation of the posterior mean in Multi-Output Gaussian Processes](https://mlsanity.com/paper/2504.21527/) (2025)

## Abstract (arXiv)

We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification. State-of-the-art (stochastic) inference for Gaussian processes on very large datasets scales cubically in the number of 'inducing inputs', variables introduced to factorise the model. Blitzkriging shares state-of-the-art scaling with data, but reduces the scaling in the number of inducing points to approximately linear. Further, in contrast to other methods, Blitzkriging: does not force the data to conform to any particular structure (including grid-like); reduces reliance on error-prone optimisation of inducing point locations; and is able to learn rich (covariance) structure from the data. We demonstrate the benefits of our approach on real data in regression, time-series prediction and signal-interpolation experiments.