In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling. These scenarios require expressive models that guarantee reliable and efficient computation of probabilistic quantities to measure uncertainty. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions, thus delivering tractable computation of the above quantities while preserving expressiveness. Building on prior work on tractable probabilistic circuits, we illustrate how CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.
翻译:在这份立场文件中,我们研究结构化产出空间的互动学习,重点是积极学习,其中标签未知,必须获得,以及怀疑学习,其中标签吵闹,可能需要重新贴标签。这些情景需要清晰的模型,保证可靠和高效地计算概率数量,以测量不确定性。我们确定一系列概率模型 -- -- 我们指CRIPS -- -- 满足所有这些条件的条件,从而提供上述数量的可移植计算,同时保持表达性。在以前关于可移动概率电路的工作的基础上,我们说明CRIPS是如何在大型结构化输出空间进行有力和高效的积极和怀疑性学习的。