# Indirect Query Bayesian Optimization with Integrated Feedback

arXiv: https://arxiv.org/abs/2412.13559 · HTML: https://mlsanity.com/paper/2412.13559/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders](https://mlsanity.com/paper/2203.12742/) (2022)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [Sequential Experimental Design for Transductive Linear Bandits](https://mlsanity.com/paper/1906.08399/) (2019)
- [Cost-aware Bayesian Optimization](https://mlsanity.com/paper/2003.10870/) (2020)
- [On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials](https://mlsanity.com/paper/1903.07082/) (2019)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Deconditional Downscaling with Gaussian Processes](https://mlsanity.com/paper/2105.12909/) (2021)
- [Bayesian optimisation under uncertain inputs](https://mlsanity.com/paper/1902.07908/) (2019)
- [Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs](https://mlsanity.com/paper/2111.06537/) (2021)
- [Optimization as Estimation with Gaussian Processes in Bandit Settings](https://mlsanity.com/paper/1510.06423/) (2015)
- [Variational Learning on Aggregate Outputs with Gaussian Processes](https://mlsanity.com/paper/1805.08463/) (2018)
- [Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications](https://mlsanity.com/paper/2302.03683/) (2023)
- [Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces](https://mlsanity.com/paper/2205.13627/) (2022)
- [Gaussian Process Bandits with Aggregated Feedback](https://mlsanity.com/paper/2112.13029/) (2021)

## Built on by (most cited first, 1 of 1)

- [Out-Of-The-Loop Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2608.04113/) (2026)

## Abstract (arXiv)

We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function f to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of f by adaptively querying and observing in the space transformed by the conditional distribution. This is motivated by real-world applications where one cannot access direct feedback due to privacy, hardware or computational constraints. We propose the Conditional Max-Value Entropy Search (CMES) acquisition function to address this novel setting, and propose a hierarchical search algorithm with multi-resolution feedback to improve computational efficiency. We show regret bounds for our proposed methods and demonstrate the effectiveness of our approaches on simulated optimization tasks.