Continual Human-in-the-Loop Optimization

Research Background and Problem

• Problem or Challenge: The optimal configuration of interactive input devices needs to meet the personalized requirements of different users. However, current systems often rely on manual tuning or separate calibration processes, which are inefficient. Although "Human-in-the-Loop Optimization" (HiLO) has the potential to achieve automatic optimization through real-time user data, it is time-consuming and fails to effectively utilize existing user data to enhance efficiency. Furthermore, current HiLO methods require optimization from scratch for each user, making it difficult to adapt to continual learning environments.
• Significance: With the widespread application of Virtual Reality (VR) and Augmented Reality (AR) technologies, personalized input systems play a significant role in improving user experience and productivity. For example, optimizing the size and position of virtual keyboards in VR can significantly enhance input speed.
• Research Motivation and Related Work: A major issue with existing HiLO methods is their low sample efficiency and inability to fully leverage prior user data. While some meta-learning methods (e.g., meta-Bayesian optimization) attempt to address the issue of low sample efficiency, they have limited scalability and capability to handle continual user learning. Additionally, current methods fail to address challenges such as "catastrophic forgetting" and model instability caused by sequential learning.

Proposed Solution

• Proposed Method: This paper introduces a novel concept—Continual Human-in-the-Loop Optimization (CHiLO)—and a new Bayesian optimization-based method: Population-Informed Continual Bayesian Optimization (ConBO).
• Key Innovations:
  1. Population Model: A population model is constructed using Bayesian Neural Networks (BNN) to summarize user characteristics at the population level and adapt to individual needs during new user optimization.
  2. Memory Replay Strategy: A generative memory replay mechanism is employed to generate reliable training samples by storing data from prior user models, addressing the issue of forgetting.
  3. Multi-Model Hybrid Optimization: During the optimization process, recommendations from the population model and the current user's Gaussian Process (GP) model are integrated, dynamically balancing optimization based on population experience and individual user needs.
  4. Stability and Scalability Design: Variance filtering and multi-level data generation mechanisms are used to enhance model stability while controlling computational complexity.
• Implementation Steps:
  1. Population Model Design: Use BNN to predict the mean and variance of user performance, ensuring model scalability and stability.
  2. Memory Replay Mechanism: Generate data from prior users' GP models, excluding high-variance sampling points to improve data quality.
  3. User Adaptation Phase: Initially rely more on population model experience, gradually transitioning to personalized optimization by combining the current user's model.
  4. Model Update: After completing optimization for a new user, update the population model by incorporating the new user's model as part of the prior data.

Research Outcomes

• Key Results:
  1. Optimization Performance: ConBO significantly improved the speed of personalized adaptation for keyboard input (enhancing typing efficiency and reducing target regret values).
  2. Progressive Learning Capability: ConBO demonstrated an increasing trend in learning efficiency as the number of users grew.
  3. Scalability and Stability: Compared to traditional Gaussian Process methods, the BNN approach showed lower online computational costs and more stable variance predictions.
  4. Effectiveness of Memory Replay: Re-optimization tests validated that ConBO effectively retained data from early users, improving optimization performance for previously seen users.
• Advantages:
  1. More efficient adaptation performance in scenarios with user diversity and task continuity.
  2. Low computational cost, suitable for large-scale user groups.
  3. Gradual optimization with reduced reliance on random exploration.
• Experimental or Evaluation Results:
  1. Experiments demonstrated that ConBO achieved faster user input speeds in virtual keyboard optimization tasks (measured in net words per minute).
  2. Simulated tests showed that ConBO outperformed other comparative methods, such as "Direct Gaussian Process" and "Transfer Acquisition Function" (TAF), on various benchmark functions (e.g., Branin and McCormick).
  3. Results also indicated that compared to standard BO, ConBO reduced cumulative regret by approximately 46%.
• Limitations and Future Directions:
  1. Adaptation to Extreme Users: The current model may primarily cater to the needs of the majority within the population, potentially underperforming for "long-tail users."
  2. User Sequence Dependency: If early users exhibit abnormal characteristics, the model's adaptation to mainstream user needs may be slowed.
  3. Extension to Complex Optimization Environments: The current work focuses on single-objective optimization; future work could expand to multi-objective scenarios or user preference optimization.
  4. Cross-Task Knowledge Transfer: Further exploration is needed to enhance cross-task optimization capabilities in different application scenarios.

By proposing CHiLO and ConBO, this study lays the foundation for next-generation personalized interactive systems and provides an effective practical framework for exploration and implementation in complex scenarios.