New paper accepted to the Proceedings of the ACM CHI Conference on Human Factors in Computing Systems. Dr. Yi-Chi Liao will present our work at CHI’26 in April in Barcelona.
Efficient Human-in-the-Loop Optimization via Priors Learned from User Models
Yi-Chi Liao, João Belo, Hee-Seung Moon, Jürgen Steimle, Anna Maria Feit
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.
Link to article:
https://lnkd.in/e4nYASE6
https://arxiv.org/abs/2510.07754
Link to preview video:
https://lnkd.in/eg2hjQed
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