论文标题
表征贝叶斯自适应实验设计对主动学习偏见的鲁棒性
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
论文作者
论文摘要
贝叶斯自适应实验设计是一种主动学习的一种形式，它选择样本以最大程度地提高他们提供的有关不确定参数的信息。先前的工作表明，其他形式的主动学习可能会遭受主动学习偏见的困扰，在这种偏见中，无代表性的抽样导致参数估计不一致。我们表明，主动学习偏见也可能会折磨贝叶斯自适应实验设计，具体取决于模型错误指定。我们分析了估计线性模型的案例，并表明较差的错误指定意味着更严重的积极学习偏见。同时，模型类结合了更多的“噪声” - 即指定观察值较高的固有方差 - 受主动学习偏见的影响较小。最后，我们从经验上证明，来自线性模型的见解可以预测非线性环境中主动学习偏见的存在和程度，即在（模拟的）偏好学习实验中。
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.
