# Active Learning of Piecewise Gaussian Process Surrogates

arXiv: https://arxiv.org/abs/2301.08789 · HTML: https://mlsanity.com/paper/2301.08789/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Doubly Stochastic Variational Inference for Deep Gaussian Processes](https://mlsanity.com/paper/1705.08933/) (2017)
- [Local Gaussian process approximation for large computer experiments](https://mlsanity.com/paper/1303.0383/) (2013)
- [Practical heteroskedastic Gaussian process modeling for large simulation experiments](https://mlsanity.com/paper/1611.05902/) (2016)
- [Active Learning for Deep Gaussian Process Surrogates](https://mlsanity.com/paper/2012.08015/) (2020)
- [Replication or exploration? Sequential design for stochastic simulation experiments](https://mlsanity.com/paper/1710.03206/) (2017)
- [Near-Optimal Adaptive Compressed Sensing](https://mlsanity.com/paper/1306.6239/) (2013)
- [Sequential Adaptive Design for Jump Regression Estimation](https://mlsanity.com/paper/1904.01648/) (2019)
- [Massively parallel approximate Gaussian process regression](https://mlsanity.com/paper/1310.5182/) (2013)
- [Latin Hypercubes and Space-filling Designs](https://mlsanity.com/paper/2203.06334/) (2022)

## Built on by (most cited first, 5 of 5)

- [Modular Jump Gaussian Processes](https://mlsanity.com/paper/2505.15557/) (2025)
- [Informed Initialization for Bayesian Optimization and Active Learning](https://mlsanity.com/paper/2510.23681/) (2025)
- [Fixed-Confidence Multiple Change Point Identification under Bandit Feedback](https://mlsanity.com/paper/2507.08994/) (2025)
- [Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates](https://mlsanity.com/paper/2602.21408/) (2026)
- [Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection](https://mlsanity.com/paper/2609.33550/) (2026)

## Abstract (arXiv)

Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.