# Adaptive design for Gaussian process regression under censoring

arXiv: https://arxiv.org/abs/1910.05452 · HTML: https://mlsanity.com/paper/1910.05452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

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

- [Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy](https://mlsanity.com/paper/2112.00600/) (2021)
- [Amortised Experimental Design and Parameter Estimation for User Models of Pointing](https://mlsanity.com/paper/2307.09878/) (2023)
- [Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators](https://mlsanity.com/paper/2410.12690/) (2024)

## Abstract (arXiv)

A key objective in engineering problems is to predict an unknown experimental surface over an input domain. In complex physical experiments, this may be hampered by response censoring, which results in a significant loss of information. For such problems, experimental design is paramount for maximizing predictive power using a small number of expensive experimental runs. To tackle this, we propose a novel adaptive design method, called the integrated censored mean-squared error (ICMSE) method. The ICMSE method first estimates the posterior probability of a new observation being censored, then adaptively chooses design points that minimize predictive uncertainty under censoring. Adopting a Gaussian process regression model with product correlation function, the proposed ICMSE criterion is easy to evaluate, which allows for efficient design optimization. We demonstrate the effectiveness of the ICMSE design in two real-world applications on surgical planning and wafer manufacturing.