主动学习是机器学习(更普遍的说是人工智能)的一个子领域,在统计学领域也叫查询学习、最优实验设计。“学习模块”和“选择策略”是主动学习算法的2个基本且重要的模块。

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主动学习是通过对最有代表性的样本进行抽样,设计标签有效的算法。在本文中,我们提出了一种状态重新标记对抗主动学习模型(SRAAL),该模型利用标注和标记/未标记的状态信息来获得信息量最大的未标记样本。SRAAL由一个表示生成器和一个状态鉴别器组成。该生成器利用补充标注信息与传统重建信息生成样本的统一表示,将语义嵌入到整个数据表示中。然后,我们在鉴别器中设计了一个在线不确定度指标,使未标记样本具有不同的重要性。因此,我们可以根据鉴别器的预测状态来选择信息最丰富的样本。我们还设计了一个算法来初始化标记池,这使得后续的采样更加有效。在各种数据集上进行的实验表明,我们的模型优于现有的主动学习方法,并且我们的初始采样算法具有更好的性能。

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Selecting input variables or design points for statistical models has been of great interest in sequential design and active learning. Motivated by two scientific examples, this paper present a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example is compressive material imaging with the purpose of accelerating the imaging speed, and the second example is a sequential design for learning a phase diagram in chemistry. In both examples, the underlying regression functions have discontinuities, so many of the existing design optimization approaches cannot be applied for the two examples, because they mostly assume a continuous regression function. There are a few studies for estimating a discontinuous regression function from its noisy observations, but all noisy observations are typically provided in advance in these studies. In this paper, we develop a design strategy of selecting the design points for regression analysis with discontinuities. We first review the existing approaches relevant to design optimization and active learning for regression analysis and discuss their limitations in handling a discontinuous regression function. We then present our novel design strategy for a regression analysis with discontinuities: some statistical properties with a fixed design will be presented first, and then these properties will be used to propose a new criterion of selecting the design points for the regression analysis. Sequential design with the new criterion will be presented with comprehensive simulated examples, and its application to the two motivating examples will be presented.

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