# Efficient Gaussian process learning via subspace projections

arXiv: https://arxiv.org/abs/2601.16332 · HTML: https://mlsanity.com/paper/2601.16332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Understanding Probabilistic Sparse Gaussian Process Approximations](https://mlsanity.com/paper/1606.04820/) (2016)
- [Environmental Sensor Placement with Convolutional Gaussian Neural Processes](https://mlsanity.com/paper/2211.10381/) (2022)
- [Gaussian Processes for Music Audio Modelling and Content Analysis](https://mlsanity.com/paper/1606.01039/) (2016)

## Abstract (arXiv)

We propose a novel training objective for GPs constructed using lower-dimensional linear projections of the data, referred to as projected likelihood (PL). We provide a closed-form expression for the information loss related to the PL and empirically show that it can be reduced with random projections on the unit sphere. We show the superiority of the PL, in terms of accuracy and computational efficiency, over the exact GP training and the variational free energy approach to sparse GPs over different optimisers, kernels and datasets of moderately large sizes.