# Continual Human-in-the-Loop Optimization

arXiv: https://arxiv.org/abs/2503.05405 · HTML: https://mlsanity.com/paper/2503.05405/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Efficient Visual Appearance Optimization by Learning from Prior Preferences](https://mlsanity.com/paper/2507.15355/) (2025)
- [Spatula: Exploring On-Demand In-Situ Interfaces and Interaction for Attribute Control](https://mlsanity.com/paper/2607.10405/) (2026)

## Abstract (arXiv)

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization process. A more efficient approach would continually leverage data from previous users to accelerate optimization, exploiting shared traits while adapting to individual characteristics. We introduce the concept of Continual Human-in-the-Loop Optimization and a Bayesian optimization-based method that leverages a Bayesian-neural-network surrogate model to capture population-level characteristics while adapting to new users. We propose a generative replay strategy to mitigate catastrophic forgetting. We demonstrate our method by optimizing virtual reality keyboard parameters for text entry using direct touch, showing reduced adaptation times with a growing user base. Our method opens the door for next-generation personalized input systems that improve with accumulated experience.