Mixed Likelihood Variational Gaussian Processes
This work addresses the limitation of GPs in human-in-the-loop settings by leveraging auxiliary data, though it appears incremental as it builds on existing variational GP methods with a novel combination approach.
The authors tackled the problem of Gaussian processes ignoring auxiliary information like domain expertise and user confidence ratings in human-in-the-loop experiments, and they proposed mixed likelihood variational GPs to incorporate such information, demonstrating performance improvements in three real-world tasks including accelerated active learning and better model fitting.
Gaussian processes (GPs) are powerful models for human-in-the-loop experiments due to their flexibility and well-calibrated uncertainty. However, GPs modeling human responses typically ignore auxiliary information, including a priori domain expertise and non-task performance information like user confidence ratings. We propose mixed likelihood variational GPs to leverage auxiliary information, which combine multiple likelihoods in a single evidence lower bound to model multiple types of data. We demonstrate the benefits of mixing likelihoods in three real-world experiments with human participants. First, we use mixed likelihood training to impose prior knowledge constraints in GP classifiers, which accelerates active learning in a visual perception task where users are asked to identify geometric errors resulting from camera position errors in virtual reality. Second, we show that leveraging Likert scale confidence ratings by mixed likelihood training improves model fitting for haptic perception of surface roughness. Lastly, we show that Likert scale confidence ratings improve human preference learning in robot gait optimization. The modeling performance improvements found using our framework across this diverse set of applications illustrates the benefits of incorporating auxiliary information into active learning and preference learning by using mixed likelihoods to jointly model multiple inputs.