In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parameter, and no intrinsic interest in characterizing the entire data-generating process. Usually, the learner must also contend with additional sources of uncertainty or variables -- with nuisance parameters. Bayesian active learning, or sequential optimal experimental design, can straightforwardly accommodate the presence of nuisance parameters, and so is a natural active learning framework for such problems. However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference. We characterize the threat of negative interference and how it fundamentally changes the nature of the Bayesian active learner's task. We show that the extent of negative interference can be extremely large, and that accurate estimation of the nuisance parameters is critical to reducing it. The Bayesian active learner is confronted with a dilemma: whether to spend a finite acquisition budget in pursuit of estimation of the target or of the nuisance parameters. Our setting encompasses Bayesian transfer learning as a special case, and our results shed light on the phenomenon of negative transfer between learning environments.

翻译：在许多场景中，例如科学推理、优化和迁移学习，学习器具有明确定义的目标——可视为对目标参数的估计——而对表征整个数据生成过程并无内在兴趣。通常，学习器还必须应对额外的不确定性来源或变量，即干扰参数。贝叶斯主动学习（或称序贯最优实验设计）能够直接适配干扰参数的存在，因此是解决此类问题的天然主动学习框架。然而，引入干扰参数可能导致贝叶斯学习器对目标参数估计产生偏差，我们将这种现象称为负向干扰。本文刻画了负向干扰的威胁及其如何根本性地改变贝叶斯主动学习任务的本质。研究表明，负向干扰的幅度可能极大，而精确估计干扰参数对降低此干扰至关重要。贝叶斯主动学习器面临两难选择：将有限的数据采集预算用于估计目标参数还是干扰参数。我们的框架将贝叶斯迁移学习作为特例包含在内，研究结果揭示了学习环境间负迁移现象的成因。