# Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

arXiv: https://arxiv.org/abs/1511.04408 · HTML: https://mlsanity.com/paper/1511.04408/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [Non-smooth Bayesian Optimization in Tuning Problems](https://mlsanity.com/paper/2109.07563/) (2021)
- [Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems](https://mlsanity.com/paper/2303.10022/) (2023)
- [Incremental Transformer Neural Processes](https://mlsanity.com/paper/2602.18955/) (2026)
- [A Change Dynamic Model for the Online Detection of Gradual Change](https://mlsanity.com/paper/2205.01054/) (2022)

## Abstract (arXiv)

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to capture the complex statistical structure. Finally, through the use of novel methods for additive non-separable kernels, we can scale the model to large datasets. We demonstrate the model on numerical and real world data, including a large spatio-temporal disease dataset where we identify previously unknown heterogeneous changes in space and time.