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. At the same time, model classes incorporating more "noise" - i.e., specifying higher inherent variance in observations - suffer less from active learning bias. Finally, we demonstrate empirically that insights from the linear model can predict the presence and degree of active learning bias in nonlinear contexts, namely in a (simulated) preference learning experiment.
翻译:Bayesian适应性实验设计是一种积极的学习形式,它选择样本,以尽量扩大它们提供的关于不确定参数的信息。先前的工作已经表明,其他形式的积极学习可能会受到积极的学习偏差的影响,而这种偏差则导致不一致的参数估计。我们表明,积极的学习偏差也会影响Bayesian适应性实验设计,这取决于模型的偏差。我们分析估计线性模型的案例,并表明更严重的偏差意味着更严重的积极学习偏差。与此同时,包含更多“噪音”的模型班——即具体说明观测中更大的内在差异——也较少受到积极的学习偏差的影响。最后,我们从经验上表明,线性模型的洞察力可以预测非线性环境中的积极学习偏差的存在和程度,即(模拟的)偏好学习实验。