# Cost-Aware Bayesian Optimization for Prototyping Interactive Devices

arXiv: https://arxiv.org/abs/2602.01774 · HTML: https://mlsanity.com/paper/2602.01774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes](https://mlsanity.com/paper/1811.00755/) (2018)
- [Cost-aware Bayesian Optimization](https://mlsanity.com/paper/2003.10870/) (2020)
- [Sequential Gallery for Interactive Visual Design Optimization](https://mlsanity.com/paper/2005.04107/) (2020)
- [FontCraft: Multimodal Font Design Using Interactive Bayesian Optimization](https://mlsanity.com/paper/2502.11399/) (2025)
- [Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques](https://mlsanity.com/paper/2204.07641/) (2022)
- [On a New Improvement-Based Acquisition Function for Bayesian Optimization](https://mlsanity.com/paper/1808.06918/) (2018)
- [Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control](https://mlsanity.com/paper/2203.02570/) (2022)
- [OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience](https://mlsanity.com/paper/2501.06757/) (2025)
- [Efficient Human-in-the-Loop Optimization via Priors Learned from User Models](https://mlsanity.com/paper/2510.07754/) (2025)
- [Bayesian Optimization with Directionally Constrained Search](https://mlsanity.com/paper/1906.09459/) (2019)

## Abstract (arXiv)

Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only ≈70% of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.