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，学习器具有明确的目标，可将其视为对目标参数的估计，且对描述整个数据生成过程并无内在兴趣。通常，学习器还需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）能够自然地适应干扰参数的存在，因此成为此类问题的理想主动学习框架。然而，干扰参数的引入可能导致贝叶斯学习器对目标参数的估计产生偏差，我们将这一现象称为负干扰。我们刻画了负干扰的威胁及其如何从根本上改变贝叶斯主动学习任务的性质。研究表明，负干扰的程度可能极其显著，而准确估计干扰参数对于减小该效应至关重要。贝叶斯主动学习器面临两难抉择：如何在有限的采集预算中权衡对目标参数与干扰参数的估计。我们的框架将贝叶斯迁移学习作为特例纳入其中，研究结果揭示了学习环境间负迁移现象的本质。