Efficient Human-in-the-Loop Optimization via Priors Learned from User Models

TL;DR

This paper introduces HOMI, a framework that uses learned priors from synthetic user models to make human-in-the-loop optimization more efficient, especially in VR interface design, by reducing the number of required user interactions.

Contribution

The paper proposes HOMI, a novel framework that incorporates a training phase with synthetic user data to enhance real-time optimization efficiency, and introduces NAF+, a neural Bayesian optimization method trained with reinforcement learning.

Findings

HOMI reduces the number of user interactions needed for optimization.

NAF+ outperforms traditional Bayesian optimization methods in efficiency.

The approach improves VR interface adaptation speed and effectiveness.

Abstract

Human-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remains costly and often impractical. We present a conceptual framework, Human-in-the-Loop Optimization with Model-Informed Priors (HOMI), which augments human-in-the-loop optimization with a training phase where the optimizer learns adaptation strategies from diverse, synthetic user data generated with predictive models before deployment. To realize HOMI, we introduce Neural Acquisition Function+ (NAF+), a Bayesian optimization method featuring a neural acquisition function trained with reinforcement learning. NAF+ learns optimization strategies from large-scale synthetic data, improving efficiency in real-time optimization with users. We evaluate HOMI and NAF+ with mid-air keyboard optimization, a representative VR input task. Our work presents a new approach for more efficient interface adaptation by bridging in situ and in silico optimization processes.