Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias

Understand

Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters.

• Prior work has shown that other forms of active learning can suffer from active learning bias, where unrepresentative sampling leads to inconsistent parameter estimates.
• We show that active learning bias can also afflict Bayesian adaptive experimental design, depending on model misspecification.
• We analyze the case of estimating a linear model, and show that worse misspecification implies more severe active learning bias.