# Bayesian Optimization With Censored Response Data

arXiv: https://arxiv.org/abs/1310.1947 · HTML: https://mlsanity.com/paper/1310.1947/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2013.

## Built on by (most cited first, 6 of 6)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration](https://mlsanity.com/paper/2212.00333/) (2022)
- [Machine Learning for Online Algorithm Selection under Censored Feedback](https://mlsanity.com/paper/2109.06234/) (2021)
- [Large Scale Multi-Task Bayesian Optimization with Large Language Models](https://mlsanity.com/paper/2503.08131/) (2025)
- [Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression](https://mlsanity.com/paper/2204.04831/) (2022)
- [A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization](https://mlsanity.com/paper/2402.04885/) (2024)

## Abstract (arXiv)

Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available. Here, we address the problem of BO under partially right-censored response data, where in some evaluations we only obtain a lower bound on the function value. The ability to handle such response data allows us to adaptively censor costly function evaluations in minimization problems where the cost of a function evaluation corresponds to the function value. One important application giving rise to such censored data is the runtime-minimizing variant of the algorithm configuration problem: finding settings of a given parametric algorithm that minimize the runtime required for solving problem instances from a given distribution. We demonstrate that terminating slow algorithm runs prematurely and handling the resulting right-censored observations can substantially improve the state of the art in model-based algorithm configuration.